From f95462fa73fbe91a99239cf553064defefa456e6 Mon Sep 17 00:00:00 2001 From: SevgiAkten Date: Wed, 14 Aug 2024 14:09:30 +0000 Subject: [PATCH] deploy: ab871daf02709476dcb84b7364c05f29e7cf10ff --- _modules/index.html | 282 +- .../{src => pycellga}/byte_operators.html | 14 +- .../example/example_alpha_cga.html | 14 +- .../example/example_ccga.html | 14 +- .../example/example_cga.html | 14 +- .../example/example_mcccga.html | 14 +- .../example/example_sync_cga.html | 14 +- _modules/{src => pycellga}/grid.html | 12 +- .../mutation/bit_flip_mutation.html | 12 +- .../mutation/byte_mutation.html | 12 +- .../mutation/byte_mutation_random.html | 12 +- .../mutation/float_uniform_mutation.html | 12 +- .../mutation/insertion_mutation.html | 12 +- .../mutation/mutation_operator.html | 10 +- .../mutation/shuffle_mutation.html | 12 +- .../mutation/swap_mutation.html | 12 +- .../mutation/two_opt_mutation.html | 12 +- .../neighborhoods/compact_13.html | 12 +- .../neighborhoods/compact_21.html | 12 +- 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pycellga.problems.single_objective.discrete.binary.html (56%) create mode 100644 pycellga.problems.single_objective.discrete.html rename src.problems.single_objective.discrete.permutation.html => pycellga.problems.single_objective.discrete.permutation.html (58%) create mode 100644 pycellga.problems.single_objective.html rename src.recombination.html => pycellga.recombination.html (52%) rename src.selection.html => pycellga.selection.html (53%) create mode 100644 pycellga.tests.html create mode 100644 setup.html delete mode 100644 src.html delete mode 100644 src.mutation.html delete mode 100644 src.problems.html delete mode 100644 src.problems.single_objective.discrete.html delete mode 100644 src.problems.single_objective.html delete mode 100644 src.tests.html diff --git a/_modules/index.html b/_modules/index.html index 238ef20..f0aee89 100644 --- a/_modules/index.html +++ b/_modules/index.html @@ -70,147 +70,147 @@

All modules for which code is available

-
diff --git a/_modules/src/byte_operators.html b/_modules/pycellga/byte_operators.html similarity index 95% rename from _modules/src/byte_operators.html rename to _modules/pycellga/byte_operators.html index e5162cf..3700efa 100644 --- a/_modules/src/byte_operators.html +++ b/_modules/pycellga/byte_operators.html @@ -3,7 +3,7 @@ - src.byte_operators — PYCELLGA Documentation 1.0.0 documentation + pycellga.byte_operators — PYCELLGA Documentation 1.0.0 documentation @@ -61,7 +61,7 @@ @@ -70,11 +70,11 @@
-

Source code for src.byte_operators

+  
Source code for pycellga.byte_operators
 import ctypes
 
 

-[docs]
+[docs]
 def float_to_bits(float_number: float) -> list[int]:
     """
     Convert a float to its bit representation.
@@ -96,7 +96,7 @@ 
Source code for src.byte_operators
 
 
 

-[docs]
+[docs]
 def bits_to_float(bit_list: list[int]) -> float:
     """
     Convert a bit representation to its float value.
@@ -120,7 +120,7 @@ 
Source code for src.byte_operators
 
 
 

-[docs]
+[docs]
 def floats_to_bits(float_list: list[float]) -> list[int]:
     """
     Convert a list of floats to their combined bit representation.
@@ -144,7 +144,7 @@ 
Source code for src.byte_operators
 
 
 

-[docs]
+[docs]
 def bits_to_floats(bit_list: list[int]) -> list[float]:
     """
     Convert a combined bit representation back to a list of floats.
diff --git a/_modules/src/example/example_alpha_cga.html b/_modules/pycellga/example/example_alpha_cga.html
similarity index 93%
rename from _modules/src/example/example_alpha_cga.html
rename to _modules/pycellga/example/example_alpha_cga.html
index 3e13299..81e1019 100644
--- a/_modules/src/example/example_alpha_cga.html
+++ b/_modules/pycellga/example/example_alpha_cga.html
@@ -3,7 +3,7 @@
 
   
   
-  src.example.example_alpha_cga — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.example.example_alpha_cga — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.example.example_alpha_cga
    • 
+      
  • pycellga.example.example_alpha_cga
    • 
       
  • 
       
    • 
   

@@ -70,7 +70,7 @@
           

            

              
-  
Source code for src.example.example_alpha_cga
+  
Source code for pycellga.example.example_alpha_cga
 import sys
 import os
 sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
@@ -80,7 +80,7 @@ 
Source code for src.example.example_alpha_cga
from numpy import power as pw
 
 

-[docs]
+[docs]
 class ExampleProblem:
     """
     Example problem class to be minimized.
@@ -90,13 +90,13 @@ 
Source code for src.example.example_alpha_cga
    """
     
 

-[docs]
+[docs]
     def __init__(self):
         pass

 
     
 

-[docs]
+[docs]
     def f(self, x):
         """
         Compute the objective function value.
@@ -118,7 +118,7 @@ 
Source code for src.example.example_alpha_cga

-[docs]
+[docs]
 def run_alpha_cga_example():
     """
     Run the Alpha Cellular Genetic Algorithm (alpha_cga) using the optimizer module.
diff --git a/_modules/src/example/example_ccga.html b/_modules/pycellga/example/example_ccga.html
similarity index 93%
rename from _modules/src/example/example_ccga.html
rename to _modules/pycellga/example/example_ccga.html
index 900354c..0bb9579 100644
--- a/_modules/src/example/example_ccga.html
+++ b/_modules/pycellga/example/example_ccga.html
@@ -3,7 +3,7 @@
 
   
   
-  src.example.example_ccga — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.example.example_ccga — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.example.example_ccga
    • 
+      
  • pycellga.example.example_ccga
    • 
       
  • 
       
    • 
   

@@ -70,7 +70,7 @@
           

            

              
-  
Source code for src.example.example_ccga
+  
Source code for pycellga.example.example_ccga
 import sys
 import os
 sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
@@ -79,7 +79,7 @@ 
Source code for src.example.example_ccga
 from individual import GeneType
 
 

-[docs]
+[docs]
 class ExampleProblem:
     """
     Example problem class to be minimized.
@@ -88,13 +88,13 @@ 
Source code for src.example.example_ccga
     """
     
 

-[docs]
+[docs]
     def __init__(self):
         pass

 
     
 

-[docs]
+[docs]
     def f(self, x):
         """
         Compute the objective function value.
@@ -116,7 +116,7 @@ 
Source code for src.example.example_ccga
 
 
 

-[docs]
+[docs]
 def run_ccga_example():
     """
     Run the Compact Cellular Genetic Algorithm (ccga) using the optimizer module.
diff --git a/_modules/src/example/example_cga.html b/_modules/pycellga/example/example_cga.html
similarity index 93%
rename from _modules/src/example/example_cga.html
rename to _modules/pycellga/example/example_cga.html
index 38bf7fb..0022210 100644
--- a/_modules/src/example/example_cga.html
+++ b/_modules/pycellga/example/example_cga.html
@@ -3,7 +3,7 @@
 
   
   
-  src.example.example_cga — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.example.example_cga — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.example.example_cga
    • 
+      
  • pycellga.example.example_cga
    • 
       
  • 
       
    • 
   

@@ -70,7 +70,7 @@
           

            

              
-  
Source code for src.example.example_cga
+  
Source code for pycellga.example.example_cga
 import sys
 import os
 sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
@@ -79,7 +79,7 @@ 
Source code for src.example.example_cga
 from numpy import power as pw
 
 

-[docs]
+[docs]
 class ExampleProblem:
     """
     Example problem class to be minimized.
@@ -89,13 +89,13 @@ 
Source code for src.example.example_cga
     """
     
 

-[docs]
+[docs]
     def __init__(self):
         pass

 
     
 

-[docs]
+[docs]
     def f(self, x):
         """
         Compute the objective function value.
@@ -117,7 +117,7 @@ 
Source code for src.example.example_cga
 
 
 

-[docs]
+[docs]
 def run_cga_example():
     """
     Run the Cellular Genetic Algorithm (cga) using the optimizer module.
diff --git a/_modules/src/example/example_mcccga.html b/_modules/pycellga/example/example_mcccga.html
similarity index 93%
rename from _modules/src/example/example_mcccga.html
rename to _modules/pycellga/example/example_mcccga.html
index 0d58f07..05c2f44 100644
--- a/_modules/src/example/example_mcccga.html
+++ b/_modules/pycellga/example/example_mcccga.html
@@ -3,7 +3,7 @@
 
   
   
-  src.example.example_mcccga — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.example.example_mcccga — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.example.example_mcccga
    • 
+      
  • pycellga.example.example_mcccga
    • 
       
  • 
       
    • 
   

@@ -70,7 +70,7 @@
           

            

              
-  
Source code for src.example.example_mcccga
+  
Source code for pycellga.example.example_mcccga
 import sys
 import os
 sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
@@ -80,7 +80,7 @@ 
Source code for src.example.example_mcccga
 from individual import GeneType
 
 

-[docs]
+[docs]
 class RealProblem:
     """
     Example problem class to be minimized.
@@ -90,13 +90,13 @@ 
Source code for src.example.example_mcccga
     """
     
 

-[docs]
+[docs]
     def __init__(self):
         pass

 
     
 

-[docs]
+[docs]
     def f(self, x):
         """
         Compute the objective function value.
@@ -118,7 +118,7 @@ 
Source code for src.example.example_mcccga
 
 
 

-[docs]
+[docs]
 def run_mcccga_example():
     """
     Run the Machine-Coded Compact Cellular Genetic Algorithm (mcccga)
diff --git a/_modules/src/example/example_sync_cga.html b/_modules/pycellga/example/example_sync_cga.html
similarity index 93%
rename from _modules/src/example/example_sync_cga.html
rename to _modules/pycellga/example/example_sync_cga.html
index cdf8cb4..8bd5b7b 100644
--- a/_modules/src/example/example_sync_cga.html
+++ b/_modules/pycellga/example/example_sync_cga.html
@@ -3,7 +3,7 @@
 
   
   
-  src.example.example_sync_cga — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.example.example_sync_cga — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.example.example_sync_cga
    • 
+      
  • pycellga.example.example_sync_cga
    • 
       
  • 
       
    • 
   

@@ -70,7 +70,7 @@
           

            

              
-  
Source code for src.example.example_sync_cga
+  
Source code for pycellga.example.example_sync_cga
 import sys
 import os
 sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
@@ -80,7 +80,7 @@ 
Source code for src.example.example_sync_cga
from numpy import power as pw
 
 

-[docs]
+[docs]
 class ExampleProblem:
     """
     Example problem class to be minimized.
@@ -90,13 +90,13 @@ 
Source code for src.example.example_sync_cga
    """
     
 

-[docs]
+[docs]
     def __init__(self):
         pass

 
     
 

-[docs]
+[docs]
     def f(self, x):
         """
         Compute the objective function value.
@@ -118,7 +118,7 @@ 
Source code for src.example.example_sync_cga
-[docs]
+[docs]
 def run_sync_cga_example():
     """
     Run the Synchronous Cellular Genetic Algorithm (sync_cga) using the optimizer module.
diff --git a/_modules/src/grid.html b/_modules/pycellga/grid.html
similarity index 93%
rename from _modules/src/grid.html
rename to _modules/pycellga/grid.html
index 1e6dfe2..dbdd038 100644
--- a/_modules/src/grid.html
+++ b/_modules/pycellga/grid.html
@@ -3,7 +3,7 @@
 
   
   
-  src.grid — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.grid — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
@@ -70,9 +70,9 @@
           

            

              
-  
Source code for src.grid
+  
Source code for pycellga.grid
 

-[docs]
+[docs]
 class Grid:
     """
     A class to represent a 2D grid.
@@ -86,7 +86,7 @@ 
Source code for src.grid
     """
 
 

-[docs]
+[docs]
     def __init__(self, n_rows: int, n_cols: int):
         """
         Initialize the Grid with the number of rows and columns.
@@ -103,7 +103,7 @@ 
Source code for src.grid
 
 
 

-[docs]
+[docs]
     def make_2d_grid(self) -> list:
         """
         Create a 2D grid where each cell is represented by a tuple (row, column).
diff --git a/_modules/src/mutation/bit_flip_mutation.html b/_modules/pycellga/mutation/bit_flip_mutation.html
similarity index 93%
rename from _modules/src/mutation/bit_flip_mutation.html
rename to _modules/pycellga/mutation/bit_flip_mutation.html
index 1b3a14b..9734a41 100644
--- a/_modules/src/mutation/bit_flip_mutation.html
+++ b/_modules/pycellga/mutation/bit_flip_mutation.html
@@ -3,7 +3,7 @@
 
   
   
-  src.mutation.bit_flip_mutation — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.mutation.bit_flip_mutation — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.mutation.bit_flip_mutation
    • 
+      
  • pycellga.mutation.bit_flip_mutation
    • 
       
  • 
       
    • 
   

@@ -70,14 +70,14 @@
           

            

              
-  
Source code for src.mutation.bit_flip_mutation
+  
Source code for pycellga.mutation.bit_flip_mutation
 import numpy as np
 from individual import *
 from problems.abstract_problem import AbstractProblem
 from mutation.mutation_operator import MutationOperator
 
 

-[docs]
+[docs]
 class BitFlipMutation(MutationOperator):
     """
     BitFlipMutation performs a bit flip mutation on an individual in a Genetic Algorithm.
@@ -91,7 +91,7 @@ 
Source code for src.mutation.bit_flip_mutation
    """
 
 

-[docs]
+[docs]
     def __init__(self, mutation_cand: Individual = None, problem: AbstractProblem = None):
         """
         Initialize the BitFlipMutation object.
@@ -108,7 +108,7 @@ 
Source code for src.mutation.bit_flip_mutation

-[docs]
+[docs]
     def mutate(self) -> Individual:
         """
         Perform a bit flip mutation on the candidate individual.
diff --git a/_modules/src/mutation/byte_mutation.html b/_modules/pycellga/mutation/byte_mutation.html
similarity index 95%
rename from _modules/src/mutation/byte_mutation.html
rename to _modules/pycellga/mutation/byte_mutation.html
index 62c8808..38ed5b9 100644
--- a/_modules/src/mutation/byte_mutation.html
+++ b/_modules/pycellga/mutation/byte_mutation.html
@@ -3,7 +3,7 @@
 
   
   
-  src.mutation.byte_mutation — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.mutation.byte_mutation — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.mutation.byte_mutation
    • 
+      
  • pycellga.mutation.byte_mutation
    • 
       
  • 
       
    • 
   

@@ -70,7 +70,7 @@
           

            

              
-  
Source code for src.mutation.byte_mutation
+  
Source code for pycellga.mutation.byte_mutation
 import numpy as np
 from individual import *
 from problems.abstract_problem import AbstractProblem
@@ -78,7 +78,7 @@ 
Source code for src.mutation.byte_mutation
 from mutation.mutation_operator import MutationOperator
 
 

-[docs]
+[docs]
 class ByteMutation(MutationOperator):
     """
     ByteMutation operator defined in (Satman, 2013). ByteMutation performs a byte-wise mutation 
@@ -93,7 +93,7 @@ 
Source code for src.mutation.byte_mutation
     """
 
 

-[docs]
+[docs]
     def __init__(self, mutation_cand: Individual = None, problem: AbstractProblem = None):
         """
         Initialize the ByteMutation object.
@@ -110,7 +110,7 @@ 
Source code for src.mutation.byte_mutation
 
 
 

-[docs]
+[docs]
     def mutate(self) -> Individual:
         """
         Perform a byte-wise mutation on the candidate individual.
diff --git a/_modules/src/mutation/byte_mutation_random.html b/_modules/pycellga/mutation/byte_mutation_random.html
similarity index 94%
rename from _modules/src/mutation/byte_mutation_random.html
rename to _modules/pycellga/mutation/byte_mutation_random.html
index ee8c670..be8aace 100644
--- a/_modules/src/mutation/byte_mutation_random.html
+++ b/_modules/pycellga/mutation/byte_mutation_random.html
@@ -3,7 +3,7 @@
 
   
   
-  src.mutation.byte_mutation_random — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.mutation.byte_mutation_random — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.mutation.byte_mutation_random
    • 
+      
  • pycellga.mutation.byte_mutation_random
    • 
       
  • 
       
    • 
   

@@ -70,7 +70,7 @@
           

            

              
-  
Source code for src.mutation.byte_mutation_random
+  
Source code for pycellga.mutation.byte_mutation_random
 import numpy as np
 from individual import *
 from problems.abstract_problem import AbstractProblem
@@ -78,7 +78,7 @@ 
Source code for src.mutation.byte_mutation_random
from mutation.mutation_operator import MutationOperator
 
 

-[docs]
+[docs]
 class ByteMutationRandom(MutationOperator):
     """
     ByteMutationRandom operator defined in (Satman, 2013). ByteMutationRandom performs 
@@ -93,7 +93,7 @@ 
Source code for src.mutation.byte_mutation_random
    """
 
 

-[docs]
+[docs]
     def __init__(self, mutation_cand: Individual = None, problem: AbstractProblem = None):
         """
         Initialize the ByteMutationRandom object.
@@ -110,7 +110,7 @@ 
Source code for src.mutation.byte_mutation_random

-[docs]
+[docs]
     def mutate(self) -> Individual:
         """
         Perform a random byte mutation on the candidate individual.
diff --git a/_modules/src/mutation/float_uniform_mutation.html b/_modules/pycellga/mutation/float_uniform_mutation.html
similarity index 93%
rename from _modules/src/mutation/float_uniform_mutation.html
rename to _modules/pycellga/mutation/float_uniform_mutation.html
index 0cfd3b3..5585e9e 100644
--- a/_modules/src/mutation/float_uniform_mutation.html
+++ b/_modules/pycellga/mutation/float_uniform_mutation.html
@@ -3,7 +3,7 @@
 
   
   
-  src.mutation.float_uniform_mutation — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.mutation.float_uniform_mutation — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.mutation.float_uniform_mutation
    • 
+      
  • pycellga.mutation.float_uniform_mutation
    • 
       
  • 
       
    • 
   

@@ -70,14 +70,14 @@
           

            

              
-  
Source code for src.mutation.float_uniform_mutation
+  
Source code for pycellga.mutation.float_uniform_mutation
 import random
 from individual import *
 from problems.abstract_problem import AbstractProblem
 from mutation.mutation_operator import MutationOperator
 
 

-[docs]
+[docs]
 class FloatUniformMutation(MutationOperator):
     """
     FloatUniformMutation performs a uniform mutation on an individual's chromosome in a Genetic Algorithm.
@@ -91,7 +91,7 @@ 
Source code for src.mutation.float_uniform_mutation
    """
 
 

-[docs]
+[docs]
     def __init__(self, mutation_cand: Individual = None, problem: AbstractProblem = None):
         """
         Initialize the FloatUniformMutation object.
@@ -108,7 +108,7 @@ 
Source code for src.mutation.float_uniform_mutation

-[docs]
+[docs]
     def mutate(self) -> Individual:
         """
         Perform a uniform mutation on the candidate individual.
diff --git a/_modules/src/mutation/insertion_mutation.html b/_modules/pycellga/mutation/insertion_mutation.html
similarity index 94%
rename from _modules/src/mutation/insertion_mutation.html
rename to _modules/pycellga/mutation/insertion_mutation.html
index 12ac9b7..2f6e97c 100644
--- a/_modules/src/mutation/insertion_mutation.html
+++ b/_modules/pycellga/mutation/insertion_mutation.html
@@ -3,7 +3,7 @@
 
   
   
-  src.mutation.insertion_mutation — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.mutation.insertion_mutation — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.mutation.insertion_mutation
    • 
+      
  • pycellga.mutation.insertion_mutation
    • 
       
  • 
       
    • 
   

@@ -70,14 +70,14 @@
           

            

              
-  
Source code for src.mutation.insertion_mutation
+  
Source code for pycellga.mutation.insertion_mutation
 import numpy as np
 from individual import *
 from problems.abstract_problem import AbstractProblem
 from mutation.mutation_operator import MutationOperator
 
 

-[docs]
+[docs]
 class InsertionMutation(MutationOperator):
     """
     InsertionMutation performs an insertion mutation on an individual's chromosome in a Genetic Algorithm.
@@ -91,7 +91,7 @@ 
Source code for src.mutation.insertion_mutation
<
     """
 
 

-[docs]
+[docs]
     def __init__(self, mutation_cand: Individual = None, problem: AbstractProblem = None):
         """
         Initialize the InsertionMutation object.
@@ -108,7 +108,7 @@ 
Source code for src.mutation.insertion_mutation
<
 
 
 

-[docs]
+[docs]
     def mutate(self) -> Individual:
         """
         Perform an insertion mutation on the candidate individual.
diff --git a/_modules/src/mutation/mutation_operator.html b/_modules/pycellga/mutation/mutation_operator.html
similarity index 88%
rename from _modules/src/mutation/mutation_operator.html
rename to _modules/pycellga/mutation/mutation_operator.html
index 20009c9..3a26799 100644
--- a/_modules/src/mutation/mutation_operator.html
+++ b/_modules/pycellga/mutation/mutation_operator.html
@@ -3,7 +3,7 @@
 
   
   
-  src.mutation.mutation_operator — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.mutation.mutation_operator — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.mutation.mutation_operator
    • 
+      
  • pycellga.mutation.mutation_operator
    • 
       
  • 
       
    • 
   

@@ -70,12 +70,12 @@
           

            

              
-  
Source code for src.mutation.mutation_operator
+  
Source code for pycellga.mutation.mutation_operator
 

-[docs]
+[docs]
 class MutationOperator:
 

-[docs]
+[docs]
     def mutate(self):
         pass

 

diff --git a/_modules/src/mutation/shuffle_mutation.html b/_modules/pycellga/mutation/shuffle_mutation.html
similarity index 95%
rename from _modules/src/mutation/shuffle_mutation.html
rename to _modules/pycellga/mutation/shuffle_mutation.html
index cfe90c6..5415c64 100644
--- a/_modules/src/mutation/shuffle_mutation.html
+++ b/_modules/pycellga/mutation/shuffle_mutation.html
@@ -3,7 +3,7 @@
 
   
   
-  src.mutation.shuffle_mutation — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.mutation.shuffle_mutation — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.mutation.shuffle_mutation
    • 
+      
  • pycellga.mutation.shuffle_mutation
    • 
       
  • 
       
    • 
   

@@ -70,7 +70,7 @@
           

            

              
-  
Source code for src.mutation.shuffle_mutation
+  
Source code for pycellga.mutation.shuffle_mutation
 import numpy as np
 import random as rd
 from individual import *
@@ -78,7 +78,7 @@ 
Source code for src.mutation.shuffle_mutation
from mutation.mutation_operator import MutationOperator
 
 

-[docs]
+[docs]
 class ShuffleMutation(MutationOperator):
     """
     ShuffleMutation performs a shuffle mutation on an individual's chromosome in a Genetic Algorithm.
@@ -92,7 +92,7 @@ 
Source code for src.mutation.shuffle_mutation
    """
 
 

-[docs]
+[docs]
     def __init__(self, mutation_cand: Individual = None, problem: AbstractProblem = None):
         """
         Initialize the ShuffleMutation object.
@@ -109,7 +109,7 @@ 
Source code for src.mutation.shuffle_mutation

-[docs]
+[docs]
     def mutate(self) -> Individual:
         """
         Perform a shuffle mutation on the candidate individual.
diff --git a/_modules/src/mutation/swap_mutation.html b/_modules/pycellga/mutation/swap_mutation.html
similarity index 94%
rename from _modules/src/mutation/swap_mutation.html
rename to _modules/pycellga/mutation/swap_mutation.html
index b035859..75f1e89 100644
--- a/_modules/src/mutation/swap_mutation.html
+++ b/_modules/pycellga/mutation/swap_mutation.html
@@ -3,7 +3,7 @@
 
   
   
-  src.mutation.swap_mutation — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.mutation.swap_mutation — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.mutation.swap_mutation
    • 
+      
  • pycellga.mutation.swap_mutation
    • 
       
  • 
       
    • 
   

@@ -70,14 +70,14 @@
           

            

              
-  
Source code for src.mutation.swap_mutation
+  
Source code for pycellga.mutation.swap_mutation
 import numpy as np
 from individual import *
 from problems.abstract_problem import AbstractProblem
 from mutation.mutation_operator import MutationOperator
 
 

-[docs]
+[docs]
 class SwapMutation(MutationOperator):
     """
     SwapMutation performs a swap mutation on an individual's chromosome in a Genetic Algorithm.
@@ -91,7 +91,7 @@ 
Source code for src.mutation.swap_mutation
     """
 
 

-[docs]
+[docs]
     def __init__(self, mutation_cand: Individual = None, problem: AbstractProblem = None):
         """
         Initialize the SwapMutation object.
@@ -108,7 +108,7 @@ 
Source code for src.mutation.swap_mutation
 
 
 

-[docs]
+[docs]
     def mutate(self) -> Individual:
         """
         Perform a swap mutation on the candidate individual.
diff --git a/_modules/src/mutation/two_opt_mutation.html b/_modules/pycellga/mutation/two_opt_mutation.html
similarity index 94%
rename from _modules/src/mutation/two_opt_mutation.html
rename to _modules/pycellga/mutation/two_opt_mutation.html
index 6e9dbe9..5c6bb69 100644
--- a/_modules/src/mutation/two_opt_mutation.html
+++ b/_modules/pycellga/mutation/two_opt_mutation.html
@@ -3,7 +3,7 @@
 
   
   
-  src.mutation.two_opt_mutation — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.mutation.two_opt_mutation — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.mutation.two_opt_mutation
    • 
+      
  • pycellga.mutation.two_opt_mutation
    • 
       
  • 
       
    • 
   

@@ -70,14 +70,14 @@
           

            

              
-  
Source code for src.mutation.two_opt_mutation
+  
Source code for pycellga.mutation.two_opt_mutation
 import numpy as np
 from individual import *
 from problems.abstract_problem import AbstractProblem
 from mutation.mutation_operator import MutationOperator
 
 

-[docs]
+[docs]
 class TwoOptMutation(MutationOperator):
     """
     TwoOptMutation performs a 2-opt mutation on an individual's chromosome in a Genetic Algorithm.
@@ -91,7 +91,7 @@ 
Source code for src.mutation.two_opt_mutation
    """
 
 

-[docs]
+[docs]
     def __init__(self, mutation_cand: Individual = None, problem: AbstractProblem = None):
         """
         Initialize the TwoOptMutation object.
@@ -108,7 +108,7 @@ 
Source code for src.mutation.two_opt_mutation

-[docs]
+[docs]
     def mutate(self) -> Individual:
         """
         Perform a 2-opt mutation on the candidate individual.
diff --git a/_modules/src/neighborhoods/compact_13.html b/_modules/pycellga/neighborhoods/compact_13.html
similarity index 95%
rename from _modules/src/neighborhoods/compact_13.html
rename to _modules/pycellga/neighborhoods/compact_13.html
index 56607cc..10f2560 100644
--- a/_modules/src/neighborhoods/compact_13.html
+++ b/_modules/pycellga/neighborhoods/compact_13.html
@@ -3,7 +3,7 @@
 
   
   
-  src.neighborhoods.compact_13 — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.neighborhoods.compact_13 — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
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  • Module code
    • 
-      
  • src.neighborhoods.compact_13
    • 
+      
  • pycellga.neighborhoods.compact_13
    • 
       
  • 
       
    • 
   

@@ -70,9 +70,9 @@
           

            

              
-  
Source code for src.neighborhoods.compact_13
+  
Source code for pycellga.neighborhoods.compact_13
 

-[docs]
+[docs]
 class Compact13:
     """
     Compact13 calculates the positions of the 12 neighbors in a 2D grid for a given position,
@@ -89,7 +89,7 @@ 
Source code for src.neighborhoods.compact_13
    """
 
 

-[docs]
+[docs]
     def __init__(self, position, n_rows, n_cols):
         """
         Initialize the Compact13 object.
@@ -109,7 +109,7 @@ 
Source code for src.neighborhoods.compact_13
-[docs]
+[docs]
     def calculate_neighbors_positions(self) -> list:
         """
         Calculate the positions of the 12 neighbors for the given position in the grid.
diff --git a/_modules/src/neighborhoods/compact_21.html b/_modules/pycellga/neighborhoods/compact_21.html
similarity index 95%
rename from _modules/src/neighborhoods/compact_21.html
rename to _modules/pycellga/neighborhoods/compact_21.html
index b6afe26..60c5e57 100644
--- a/_modules/src/neighborhoods/compact_21.html
+++ b/_modules/pycellga/neighborhoods/compact_21.html
@@ -3,7 +3,7 @@
 
   
   
-  src.neighborhoods.compact_21 — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.neighborhoods.compact_21 — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.neighborhoods.compact_21
    • 
+      
  • pycellga.neighborhoods.compact_21
    • 
       
  • 
       
    • 
   

@@ -70,9 +70,9 @@
           

            

              
-  
Source code for src.neighborhoods.compact_21
+  
Source code for pycellga.neighborhoods.compact_21
 

-[docs]
+[docs]
 class Compact21:
     """
     Compact21 calculates the positions of the 20 neighbors in a 2D grid for a given position,
@@ -89,7 +89,7 @@ 
Source code for src.neighborhoods.compact_21
    """
 
 

-[docs]
+[docs]
     def __init__(self, position, n_rows, n_cols):
         """
         Initialize the Compact21 object.
@@ -109,7 +109,7 @@ 
Source code for src.neighborhoods.compact_21
-[docs]
+[docs]
     def calculate_neighbors_positions(self) -> list:
         """
         Calculate the positions of the 20 neighbors for the given position in the grid.
diff --git a/_modules/src/neighborhoods/compact_25.html b/_modules/pycellga/neighborhoods/compact_25.html
similarity index 95%
rename from _modules/src/neighborhoods/compact_25.html
rename to _modules/pycellga/neighborhoods/compact_25.html
index 8688a65..567385b 100644
--- a/_modules/src/neighborhoods/compact_25.html
+++ b/_modules/pycellga/neighborhoods/compact_25.html
@@ -3,7 +3,7 @@
 
   
   
-  src.neighborhoods.compact_25 — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.neighborhoods.compact_25 — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.neighborhoods.compact_25
    • 
+      
  • pycellga.neighborhoods.compact_25
    • 
       
  • 
       
    • 
   

@@ -70,9 +70,9 @@
           

            

              
-  
Source code for src.neighborhoods.compact_25
+  
Source code for pycellga.neighborhoods.compact_25
 

-[docs]
+[docs]
 class Compact25:
     """
     Compact25 calculates the positions of the 24 neighbors in a 2D grid for a given position,
@@ -89,7 +89,7 @@ 
Source code for src.neighborhoods.compact_25
    """
 
 

-[docs]
+[docs]
     def __init__(self, position, n_rows, n_cols):
         """
         Initialize the Compact25 object.
@@ -109,7 +109,7 @@ 
Source code for src.neighborhoods.compact_25
-[docs]
+[docs]
     def calculate_neighbors_positions(self) -> list:
         """
         Calculate the positions of the 24 neighbors for the given position in the grid.
diff --git a/_modules/src/neighborhoods/compact_9.html b/_modules/pycellga/neighborhoods/compact_9.html
similarity index 95%
rename from _modules/src/neighborhoods/compact_9.html
rename to _modules/pycellga/neighborhoods/compact_9.html
index fce6002..a19f227 100644
--- a/_modules/src/neighborhoods/compact_9.html
+++ b/_modules/pycellga/neighborhoods/compact_9.html
@@ -3,7 +3,7 @@
 
   
   
-  src.neighborhoods.compact_9 — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.neighborhoods.compact_9 — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.neighborhoods.compact_9
    • 
+      
  • pycellga.neighborhoods.compact_9
    • 
       
  • 
       
    • 
   

@@ -70,9 +70,9 @@
           

            

              
-  
Source code for src.neighborhoods.compact_9
+  
Source code for pycellga.neighborhoods.compact_9
 

-[docs]
+[docs]
 class Compact9:
     """
     Compact9 calculates the positions of the 8 neighbors in a 2D grid for a given position,
@@ -89,7 +89,7 @@ 
Source code for src.neighborhoods.compact_9
     """
 
 

-[docs]
+[docs]
     def __init__(self, position, n_rows, n_cols):
         """
         Initialize the Compact9 object.
@@ -109,7 +109,7 @@ 
Source code for src.neighborhoods.compact_9
 
 
 

-[docs]
+[docs]
     def calculate_neighbors_positions(self) -> list:
         """
         Calculate the positions of the 8 neighbors for the given position in the grid.
diff --git a/_modules/src/neighborhoods/linear_5.html b/_modules/pycellga/neighborhoods/linear_5.html
similarity index 94%
rename from _modules/src/neighborhoods/linear_5.html
rename to _modules/pycellga/neighborhoods/linear_5.html
index 3f3068f..080a15f 100644
--- a/_modules/src/neighborhoods/linear_5.html
+++ b/_modules/pycellga/neighborhoods/linear_5.html
@@ -3,7 +3,7 @@
 
   
   
-  src.neighborhoods.linear_5 — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.neighborhoods.linear_5 — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.neighborhoods.linear_5
    • 
+      
  • pycellga.neighborhoods.linear_5
    • 
       
  • 
       
    • 
   

@@ -70,9 +70,9 @@
           

            

              
-  
Source code for src.neighborhoods.linear_5
+  
Source code for pycellga.neighborhoods.linear_5
 

-[docs]
+[docs]
 class Linear5:
     """
     Linear5 calculates the positions of the 4 neighbors in a 2D grid for a given position,
@@ -89,7 +89,7 @@ 
Source code for src.neighborhoods.linear_5
     """
 
 

-[docs]
+[docs]
     def __init__(self, position, n_rows, n_cols):
         """
         Initialize the Linear5 object.
@@ -109,7 +109,7 @@ 
Source code for src.neighborhoods.linear_5
 
 
 

-[docs]
+[docs]
     def calculate_neighbors_positions(self) -> list:
         """
         Calculate the positions of the 4 neighbors for the given position in the grid.
diff --git a/_modules/src/neighborhoods/linear_9.html b/_modules/pycellga/neighborhoods/linear_9.html
similarity index 95%
rename from _modules/src/neighborhoods/linear_9.html
rename to _modules/pycellga/neighborhoods/linear_9.html
index 687956a..a380e60 100644
--- a/_modules/src/neighborhoods/linear_9.html
+++ b/_modules/pycellga/neighborhoods/linear_9.html
@@ -3,7 +3,7 @@
 
   
   
-  src.neighborhoods.linear_9 — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.neighborhoods.linear_9 — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.neighborhoods.linear_9
    • 
+      
  • pycellga.neighborhoods.linear_9
    • 
       
  • 
       
    • 
   

@@ -70,9 +70,9 @@
           

            

              
-  
Source code for src.neighborhoods.linear_9
+  
Source code for pycellga.neighborhoods.linear_9
 

-[docs]
+[docs]
 class Linear9:
     """
     Linear9 calculates the positions of the 8 neighbors in a 2D grid for a given position,
@@ -89,7 +89,7 @@ 
Source code for src.neighborhoods.linear_9
     """
 
 

-[docs]
+[docs]
     def __init__(self, position, n_rows, n_cols):
         """
         Initialize the Linear9 object.
@@ -109,7 +109,7 @@ 
Source code for src.neighborhoods.linear_9
 
 
 

-[docs]
+[docs]
     def calculate_neighbors_positions(self) -> list:
         """
         Calculate the positions of the 8 neighbors for the given position in the grid.
diff --git a/_modules/src/problems/abstract_problem.html b/_modules/pycellga/problems/abstract_problem.html
similarity index 90%
rename from _modules/src/problems/abstract_problem.html
rename to _modules/pycellga/problems/abstract_problem.html
index 40c7c71..761f304 100644
--- a/_modules/src/problems/abstract_problem.html
+++ b/_modules/pycellga/problems/abstract_problem.html
@@ -3,7 +3,7 @@
 
   
   
-  src.problems.abstract_problem — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.problems.abstract_problem — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.problems.abstract_problem
    • 
+      
  • pycellga.problems.abstract_problem
    • 
       
  • 
       
    • 
   

@@ -70,9 +70,9 @@
           

            

              
-  
Source code for src.problems.abstract_problem
+  
Source code for pycellga.problems.abstract_problem
 

-[docs]
+[docs]
 class AbstractProblem:
     """
     An abstract base class for optimization problems.
@@ -84,7 +84,7 @@ 
Source code for src.problems.abstract_problem
    """
 
 

-[docs]
+[docs]
     def f(self, x):
         """
         Evaluate the fitness of a given solution x.
diff --git a/_modules/src/problems/single_objective/continuous/ackley.html b/_modules/pycellga/problems/single_objective/continuous/ackley.html
similarity index 92%
rename from _modules/src/problems/single_objective/continuous/ackley.html
rename to _modules/pycellga/problems/single_objective/continuous/ackley.html
index 11a6b49..a8bd526 100644
--- a/_modules/src/problems/single_objective/continuous/ackley.html
+++ b/_modules/pycellga/problems/single_objective/continuous/ackley.html
@@ -3,7 +3,7 @@
 
   
   
-  src.problems.single_objective.continuous.ackley — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.problems.single_objective.continuous.ackley — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.problems.single_objective.continuous.ackley
    • 
+      
  • pycellga.problems.single_objective.continuous.ackley
    • 
       
  • 
       
    • 
   

@@ -70,11 +70,11 @@
           

            

              
-  
Source code for src.problems.single_objective.continuous.ackley
+  
Source code for pycellga.problems.single_objective.continuous.ackley
 from numpy import pi, e, cos, sqrt, exp
 from problems.abstract_problem import AbstractProblem
 

-[docs]
+[docs]
 class Ackley(AbstractProblem):
     """
     Ackley function implementation for optimization problems.
@@ -99,7 +99,7 @@ 
Source code for src.problems.single_objective.continuous.ackley
    """
 
 

-[docs]
+[docs]
     def f(self, x: list) -> float:
         """
         Calculate the Ackley function value for a given list of variables.
diff --git a/_modules/src/problems/single_objective/continuous/bentcigar.html b/_modules/pycellga/problems/single_objective/continuous/bentcigar.html
similarity index 91%
rename from _modules/src/problems/single_objective/continuous/bentcigar.html
rename to _modules/pycellga/problems/single_objective/continuous/bentcigar.html
index 23ec716..c0456e7 100644
--- a/_modules/src/problems/single_objective/continuous/bentcigar.html
+++ b/_modules/pycellga/problems/single_objective/continuous/bentcigar.html
@@ -3,7 +3,7 @@
 
   
   
-  src.problems.single_objective.continuous.bentcigar — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.problems.single_objective.continuous.bentcigar — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.problems.single_objective.continuous.bentcigar
    • 
+      
  • pycellga.problems.single_objective.continuous.bentcigar
    • 
       
  • 
       
    • 
   

@@ -70,12 +70,12 @@
           

            

              
-  
Source code for src.problems.single_objective.continuous.bentcigar
+  
Source code for pycellga.problems.single_objective.continuous.bentcigar
 from problems.abstract_problem import AbstractProblem
 from mpmath import power as pw
 
 

-[docs]
+[docs]
 class Bentcigar(AbstractProblem):
     """
     Bentcigar function implementation for optimization problems.
@@ -99,7 +99,7 @@ 
Source code for src.problems.single_objective.continuous.bentcigar
    """
 
 

-[docs]
+[docs]
     def f(self, X: list) -> float:
         """
         Calculate the Bentcigar function value for a given list of variables.
diff --git a/_modules/src/problems/single_objective/continuous/bohachevsky.html b/_modules/pycellga/problems/single_objective/continuous/bohachevsky.html
similarity index 92%
rename from _modules/src/problems/single_objective/continuous/bohachevsky.html
rename to _modules/pycellga/problems/single_objective/continuous/bohachevsky.html
index 08a3ce8..fdb3e88 100644
--- a/_modules/src/problems/single_objective/continuous/bohachevsky.html
+++ b/_modules/pycellga/problems/single_objective/continuous/bohachevsky.html
@@ -3,7 +3,7 @@
 
   
   
-  src.problems.single_objective.continuous.bohachevsky — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.problems.single_objective.continuous.bohachevsky — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.problems.single_objective.continuous.bohachevsky
    • 
+      
  • pycellga.problems.single_objective.continuous.bohachevsky
    • 
       
  • 
       
    • 
   

@@ -70,14 +70,14 @@
           

            

              
-  
Source code for src.problems.single_objective.continuous.bohachevsky
+  
Source code for pycellga.problems.single_objective.continuous.bohachevsky
 from numpy import cos
 from numpy import pi
 from mpmath import power as pw
 from problems.abstract_problem import AbstractProblem
 
 

-[docs]
+[docs]
 class Bohachevsky(AbstractProblem):
     """
     Bohachevsky function implementation for optimization problems.
@@ -101,7 +101,7 @@ 
Source code for src.problems.single_objective.continuous.bohachevsky
    """
 
 

-[docs]
+[docs]
     def f(self, x: list) -> float:
         """
         Calculate the Bohachevsky function value for a given list of variables.
diff --git a/_modules/src/problems/single_objective/continuous/chichinadze.html b/_modules/pycellga/problems/single_objective/continuous/chichinadze.html
similarity index 92%
rename from _modules/src/problems/single_objective/continuous/chichinadze.html
rename to _modules/pycellga/problems/single_objective/continuous/chichinadze.html
index a1dfcb3..047de4c 100644
--- a/_modules/src/problems/single_objective/continuous/chichinadze.html
+++ b/_modules/pycellga/problems/single_objective/continuous/chichinadze.html
@@ -3,7 +3,7 @@
 
   
   
-  src.problems.single_objective.continuous.chichinadze — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.problems.single_objective.continuous.chichinadze — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.problems.single_objective.continuous.chichinadze
    • 
+      
  • pycellga.problems.single_objective.continuous.chichinadze
    • 
       
  • 
       
    • 
   

@@ -70,12 +70,12 @@
           

            

              
-  
Source code for src.problems.single_objective.continuous.chichinadze
+  
Source code for pycellga.problems.single_objective.continuous.chichinadze
 from problems.abstract_problem import AbstractProblem
 import numpy as np
 
 

-[docs]
+[docs]
 class Chichinadze(AbstractProblem):
     """
     Chichinadze function implementation for optimization problems.
@@ -99,7 +99,7 @@ 
Source code for src.problems.single_objective.continuous.chichinadze
    """
 
 

-[docs]
+[docs]
     def f(self, X: list) -> float:
         """
         Calculate the Chichinadze function value for a given list of variables.
diff --git a/_modules/src/problems/single_objective/continuous/dropwave.html b/_modules/pycellga/problems/single_objective/continuous/dropwave.html
similarity index 92%
rename from _modules/src/problems/single_objective/continuous/dropwave.html
rename to _modules/pycellga/problems/single_objective/continuous/dropwave.html
index 60878b8..ddf46e7 100644
--- a/_modules/src/problems/single_objective/continuous/dropwave.html
+++ b/_modules/pycellga/problems/single_objective/continuous/dropwave.html
@@ -3,7 +3,7 @@
 
   
   
-  src.problems.single_objective.continuous.dropwave — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.problems.single_objective.continuous.dropwave — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.problems.single_objective.continuous.dropwave
    • 
+      
  • pycellga.problems.single_objective.continuous.dropwave
    • 
       
  • 
       
    • 
   

@@ -70,7 +70,7 @@
           

            

              
-  
Source code for src.problems.single_objective.continuous.dropwave
+  
Source code for pycellga.problems.single_objective.continuous.dropwave
 from problems.abstract_problem import AbstractProblem
 import math
 from numpy import *
@@ -79,7 +79,7 @@ 
Source code for src.problems.single_objective.continuous.dropwave
# Global minimum at f(0,0) = −1
 
 

-[docs]
+[docs]
 class Dropwave(AbstractProblem):
     """
     Dropwave function for optimization problems.
@@ -94,7 +94,7 @@ 
Source code for src.problems.single_objective.continuous.dropwave
    """
 
 

-[docs]
+[docs]
     def f(self, x: list) -> float:
         """
         Evaluate the Dropwave function at a given point.
diff --git a/_modules/src/problems/single_objective/continuous/fms.html b/_modules/pycellga/problems/single_objective/continuous/fms.html
similarity index 94%
rename from _modules/src/problems/single_objective/continuous/fms.html
rename to _modules/pycellga/problems/single_objective/continuous/fms.html
index 971bd10..932fd63 100644
--- a/_modules/src/problems/single_objective/continuous/fms.html
+++ b/_modules/pycellga/problems/single_objective/continuous/fms.html
@@ -3,7 +3,7 @@
 
   
   
-  src.problems.single_objective.continuous.fms — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.problems.single_objective.continuous.fms — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.problems.single_objective.continuous.fms
    • 
+      
  • pycellga.problems.single_objective.continuous.fms
    • 
       
  • 
       
    • 
   

@@ -70,12 +70,12 @@
           

            

              
-  
Source code for src.problems.single_objective.continuous.fms
+  
Source code for pycellga.problems.single_objective.continuous.fms
 from problems.abstract_problem import AbstractProblem
 from numpy import pi, sin, random
 
 

-[docs]
+[docs]
 class Fms(AbstractProblem):
     """
     Fms function implementation for optimization problems.
@@ -100,7 +100,7 @@ 
Source code for src.problems.single_objective.continuous.fms
    """
 
 

-[docs]
+[docs]
     def f(self, x: list) -> float:
         """
         Calculate the Fms function value for a given list of variables.
diff --git a/_modules/src/problems/single_objective/continuous/griewank.html b/_modules/pycellga/problems/single_objective/continuous/griewank.html
similarity index 91%
rename from _modules/src/problems/single_objective/continuous/griewank.html
rename to _modules/pycellga/problems/single_objective/continuous/griewank.html
index 2bffbc5..f95cde0 100644
--- a/_modules/src/problems/single_objective/continuous/griewank.html
+++ b/_modules/pycellga/problems/single_objective/continuous/griewank.html
@@ -3,7 +3,7 @@
 
   
   
-  src.problems.single_objective.continuous.griewank — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.problems.single_objective.continuous.griewank — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.problems.single_objective.continuous.griewank
    • 
+      
  • pycellga.problems.single_objective.continuous.griewank
    • 
       
  • 
       
    • 
   

@@ -70,12 +70,12 @@
           

            

              
-  
Source code for src.problems.single_objective.continuous.griewank
+  
Source code for pycellga.problems.single_objective.continuous.griewank
 from problems.abstract_problem import AbstractProblem
 import math
 
 

-[docs]
+[docs]
 class Griewank(AbstractProblem):
     """
     Griewank function implementation for optimization problems.
@@ -95,7 +95,7 @@ 
Source code for src.problems.single_objective.continuous.griewank
    """
 
 

-[docs]
+[docs]
     def f(self, X: list) -> float:
         """
         Calculate the Griewank function value for a given list of variables.
diff --git a/_modules/src/problems/single_objective/continuous/holzman.html b/_modules/pycellga/problems/single_objective/continuous/holzman.html
similarity index 91%
rename from _modules/src/problems/single_objective/continuous/holzman.html
rename to _modules/pycellga/problems/single_objective/continuous/holzman.html
index 42723ac..a43f33f 100644
--- a/_modules/src/problems/single_objective/continuous/holzman.html
+++ b/_modules/pycellga/problems/single_objective/continuous/holzman.html
@@ -3,7 +3,7 @@
 
   
   
-  src.problems.single_objective.continuous.holzman — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.problems.single_objective.continuous.holzman — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.problems.single_objective.continuous.holzman
    • 
+      
  • pycellga.problems.single_objective.continuous.holzman
    • 
       
  • 
       
    • 
   

@@ -70,12 +70,12 @@
           

            

              
-  
Source code for src.problems.single_objective.continuous.holzman
+  
Source code for pycellga.problems.single_objective.continuous.holzman
 from problems.abstract_problem import AbstractProblem
 from mpmath import power as pw
 
 

-[docs]
+[docs]
 class Holzman(AbstractProblem):
     """
     Holzman function implementation for optimization problems.
@@ -99,7 +99,7 @@ 
Source code for src.problems.single_objective.continuous.holzman
    """
 
 

-[docs]
+[docs]
     def f(self, x: list) -> float:
         """
         Calculate the Holzman function value for a given list of variables.
diff --git a/_modules/src/problems/single_objective/continuous/levy.html b/_modules/pycellga/problems/single_objective/continuous/levy.html
similarity index 93%
rename from _modules/src/problems/single_objective/continuous/levy.html
rename to _modules/pycellga/problems/single_objective/continuous/levy.html
index 0cd1a71..00cfbde 100644
--- a/_modules/src/problems/single_objective/continuous/levy.html
+++ b/_modules/pycellga/problems/single_objective/continuous/levy.html
@@ -3,7 +3,7 @@
 
   
   
-  src.problems.single_objective.continuous.levy — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.problems.single_objective.continuous.levy — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.problems.single_objective.continuous.levy
    • 
+      
  • pycellga.problems.single_objective.continuous.levy
    • 
       
  • 
       
    • 
   

@@ -70,13 +70,13 @@
           

            

              
-  
Source code for src.problems.single_objective.continuous.levy
+  
Source code for pycellga.problems.single_objective.continuous.levy
 from problems.abstract_problem import AbstractProblem
 import math
 from mpmath import power as pw
 
 

-[docs]
+[docs]
 class Levy(AbstractProblem):
     """
     Levy function implementation for optimization problems.
@@ -100,7 +100,7 @@ 
Source code for src.problems.single_objective.continuous.levy
    """
 
 

-[docs]
+[docs]
     def f(self, x: list) -> float:
         """
         Calculate the Levy function value for a given list of variables.
diff --git a/_modules/src/problems/single_objective/continuous/matyas.html b/_modules/pycellga/problems/single_objective/continuous/matyas.html
similarity index 92%
rename from _modules/src/problems/single_objective/continuous/matyas.html
rename to _modules/pycellga/problems/single_objective/continuous/matyas.html
index baa1443..3d2a054 100644
--- a/_modules/src/problems/single_objective/continuous/matyas.html
+++ b/_modules/pycellga/problems/single_objective/continuous/matyas.html
@@ -3,7 +3,7 @@
 
   
   
-  src.problems.single_objective.continuous.matyas — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.problems.single_objective.continuous.matyas — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.problems.single_objective.continuous.matyas
    • 
+      
  • pycellga.problems.single_objective.continuous.matyas
    • 
       
  • 
       
    • 
   

@@ -70,12 +70,12 @@
           

            

              
-  
Source code for src.problems.single_objective.continuous.matyas
+  
Source code for pycellga.problems.single_objective.continuous.matyas
 from problems.abstract_problem import AbstractProblem
 from mpmath import power as pw
 
 

-[docs]
+[docs]
 class Matyas(AbstractProblem):
     """
     Matyas function implementation for optimization problems.
@@ -99,7 +99,7 @@ 
Source code for src.problems.single_objective.continuous.matyas
    """
 
 

-[docs]
+[docs]
     def f(self, X: list) -> float:
         """
         Calculate the Matyas function value for a given list of variables.
diff --git a/_modules/src/problems/single_objective/continuous/pow.html b/_modules/pycellga/problems/single_objective/continuous/pow.html
similarity index 92%
rename from _modules/src/problems/single_objective/continuous/pow.html
rename to _modules/pycellga/problems/single_objective/continuous/pow.html
index 42afcc8..f8a6a0d 100644
--- a/_modules/src/problems/single_objective/continuous/pow.html
+++ b/_modules/pycellga/problems/single_objective/continuous/pow.html
@@ -3,7 +3,7 @@
 
   
   
-  src.problems.single_objective.continuous.pow — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.problems.single_objective.continuous.pow — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.problems.single_objective.continuous.pow
    • 
+      
  • pycellga.problems.single_objective.continuous.pow
    • 
       
  • 
       
    • 
   

@@ -70,12 +70,12 @@
           

            

              
-  
Source code for src.problems.single_objective.continuous.pow
+  
Source code for pycellga.problems.single_objective.continuous.pow
 from problems.abstract_problem import AbstractProblem
 from mpmath import power as pw
 
 

-[docs]
+[docs]
 class Pow(AbstractProblem):
     """
     Pow function implementation for optimization problems.
@@ -99,7 +99,7 @@ 
Source code for src.problems.single_objective.continuous.pow
    """
 
 

-[docs]
+[docs]
     def f(self, x: list) -> float:
         """
         Calculate the Pow function value for a given list of variables.
diff --git a/_modules/src/problems/single_objective/continuous/powell.html b/_modules/pycellga/problems/single_objective/continuous/powell.html
similarity index 93%
rename from _modules/src/problems/single_objective/continuous/powell.html
rename to _modules/pycellga/problems/single_objective/continuous/powell.html
index 6499d27..0ae8e69 100644
--- a/_modules/src/problems/single_objective/continuous/powell.html
+++ b/_modules/pycellga/problems/single_objective/continuous/powell.html
@@ -3,7 +3,7 @@
 
   
   
-  src.problems.single_objective.continuous.powell — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.problems.single_objective.continuous.powell — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.problems.single_objective.continuous.powell
    • 
+      
  • pycellga.problems.single_objective.continuous.powell
    • 
       
  • 
       
    • 
   

@@ -70,13 +70,13 @@
           

            

              
-  
Source code for src.problems.single_objective.continuous.powell
+  
Source code for pycellga.problems.single_objective.continuous.powell
 from problems.abstract_problem import AbstractProblem
 from mpmath import power as pw
 
 
 

-[docs]
+[docs]
 class Powell(AbstractProblem):
     """
     Powell function implementation for optimization problems.
@@ -100,7 +100,7 @@ 
Source code for src.problems.single_objective.continuous.powell
    """
 
 

-[docs]
+[docs]
     def f(self, x: list) -> float:
         """
         Calculate the Powell function value for a given list of variables.
diff --git a/_modules/src/problems/single_objective/continuous/rastrigin.html b/_modules/pycellga/problems/single_objective/continuous/rastrigin.html
similarity index 91%
rename from _modules/src/problems/single_objective/continuous/rastrigin.html
rename to _modules/pycellga/problems/single_objective/continuous/rastrigin.html
index e2bafbe..bf288ab 100644
--- a/_modules/src/problems/single_objective/continuous/rastrigin.html
+++ b/_modules/pycellga/problems/single_objective/continuous/rastrigin.html
@@ -3,7 +3,7 @@
 
   
   
-  src.problems.single_objective.continuous.rastrigin — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.problems.single_objective.continuous.rastrigin — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.problems.single_objective.continuous.rastrigin
    • 
+      
  • pycellga.problems.single_objective.continuous.rastrigin
    • 
       
  • 
       
    • 
   

@@ -70,11 +70,11 @@
           

            

              
-  
Source code for src.problems.single_objective.continuous.rastrigin
+  
Source code for pycellga.problems.single_objective.continuous.rastrigin
 from numpy import cos, pi
 from problems.abstract_problem import AbstractProblem
 

-[docs]
+[docs]
 class Rastrigin(AbstractProblem):
     """
     Rastrigin function implementation for optimization problems.
@@ -98,7 +98,7 @@ 
Source code for src.problems.single_objective.continuous.rastrigin
    """
 
 

-[docs]
+[docs]
     def f(self, x: list) -> float:
         """
         Calculate the Rastrigin function value for a given list of variables.
diff --git a/_modules/src/problems/single_objective/continuous/rosenbrock.html b/_modules/pycellga/problems/single_objective/continuous/rosenbrock.html
similarity index 91%
rename from _modules/src/problems/single_objective/continuous/rosenbrock.html
rename to _modules/pycellga/problems/single_objective/continuous/rosenbrock.html
index 481fcaf..266cf28 100644
--- a/_modules/src/problems/single_objective/continuous/rosenbrock.html
+++ b/_modules/pycellga/problems/single_objective/continuous/rosenbrock.html
@@ -3,7 +3,7 @@
 
   
   
-  src.problems.single_objective.continuous.rosenbrock — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.problems.single_objective.continuous.rosenbrock — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.problems.single_objective.continuous.rosenbrock
    • 
+      
  • pycellga.problems.single_objective.continuous.rosenbrock
    • 
       
  • 
       
    • 
   

@@ -70,11 +70,11 @@
           

            

              
-  
Source code for src.problems.single_objective.continuous.rosenbrock
+  
Source code for pycellga.problems.single_objective.continuous.rosenbrock
 from problems.abstract_problem import AbstractProblem
 from mpmath import power as pw
 

-[docs]
+[docs]
 class Rosenbrock(AbstractProblem):
     """
     Rosenbrock function implementation for optimization problems.
@@ -98,7 +98,7 @@ 
Source code for src.problems.single_objective.continuous.rosenbrock
    """
 
 

-[docs]
+[docs]
     def f(self, x: list) -> float:
         """
         Calculate the Rosenbrock function value for a given list of variables.
diff --git a/_modules/src/problems/single_objective/continuous/rothellipsoid.html b/_modules/pycellga/problems/single_objective/continuous/rothellipsoid.html
similarity index 91%
rename from _modules/src/problems/single_objective/continuous/rothellipsoid.html
rename to _modules/pycellga/problems/single_objective/continuous/rothellipsoid.html
index 2faf2bb..1f85974 100644
--- a/_modules/src/problems/single_objective/continuous/rothellipsoid.html
+++ b/_modules/pycellga/problems/single_objective/continuous/rothellipsoid.html
@@ -3,7 +3,7 @@
 
   
   
-  src.problems.single_objective.continuous.rothellipsoid — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.problems.single_objective.continuous.rothellipsoid — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.problems.single_objective.continuous.rothellipsoid
    • 
+      
  • pycellga.problems.single_objective.continuous.rothellipsoid
    • 
       
  • 
       
    • 
   

@@ -70,12 +70,12 @@
           

            

              
-  
Source code for src.problems.single_objective.continuous.rothellipsoid
+  
Source code for pycellga.problems.single_objective.continuous.rothellipsoid
 from problems.abstract_problem import AbstractProblem
 from mpmath import power as pw
 
 

-[docs]
+[docs]
 class Rothellipsoid(AbstractProblem):
     """
     Rotated Hyper-Ellipsoid function implementation for optimization problems.
@@ -99,7 +99,7 @@ 
Source code for src.problems.single_objective.continuous.rothellipsoid
<
     """
 
 

-[docs]
+[docs]
     def f(self, x: list) -> float:
         """
         Calculate the Rotated Hyper-Ellipsoid function value for a given list of variables.
diff --git a/_modules/src/problems/single_objective/continuous/schaffer.html b/_modules/pycellga/problems/single_objective/continuous/schaffer.html
similarity index 92%
rename from _modules/src/problems/single_objective/continuous/schaffer.html
rename to _modules/pycellga/problems/single_objective/continuous/schaffer.html
index 42f5af0..cefcb3f 100644
--- a/_modules/src/problems/single_objective/continuous/schaffer.html
+++ b/_modules/pycellga/problems/single_objective/continuous/schaffer.html
@@ -3,7 +3,7 @@
 
   
   
-  src.problems.single_objective.continuous.schaffer — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.problems.single_objective.continuous.schaffer — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.problems.single_objective.continuous.schaffer
    • 
+      
  • pycellga.problems.single_objective.continuous.schaffer
    • 
       
  • 
       
    • 
   

@@ -70,14 +70,14 @@
           

            

              
-  
Source code for src.problems.single_objective.continuous.schaffer
+  
Source code for pycellga.problems.single_objective.continuous.schaffer
 import numpy as np
 from mpmath import power as pw
 from problems.abstract_problem import AbstractProblem
 
 
 

-[docs]
+[docs]
 class Schaffer(AbstractProblem):
     """
     Schaffer's Function.
@@ -92,7 +92,7 @@ 
Source code for src.problems.single_objective.continuous.schaffer
    """
 
 

-[docs]
+[docs]
     def f(self, X: list) -> float:
         """
         Evaluate the Schaffer's function at a given point.
diff --git a/_modules/src/problems/single_objective/continuous/schaffer2.html b/_modules/pycellga/problems/single_objective/continuous/schaffer2.html
similarity index 92%
rename from _modules/src/problems/single_objective/continuous/schaffer2.html
rename to _modules/pycellga/problems/single_objective/continuous/schaffer2.html
index d1ed242..f34dfe4 100644
--- a/_modules/src/problems/single_objective/continuous/schaffer2.html
+++ b/_modules/pycellga/problems/single_objective/continuous/schaffer2.html
@@ -3,7 +3,7 @@
 
   
   
-  src.problems.single_objective.continuous.schaffer2 — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.problems.single_objective.continuous.schaffer2 — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.problems.single_objective.continuous.schaffer2
    • 
+      
  • pycellga.problems.single_objective.continuous.schaffer2
    • 
       
  • 
       
    • 
   

@@ -70,14 +70,14 @@
           

            

              
-  
Source code for src.problems.single_objective.continuous.schaffer2
+  
Source code for pycellga.problems.single_objective.continuous.schaffer2
 import numpy as np
 from numpy import power as pw
 from problems.abstract_problem import AbstractProblem
 
 
 

-[docs]
+[docs]
 class Schaffer2(AbstractProblem):
     """
     Modified Schaffer function #2 implementation for optimization problems.
@@ -101,7 +101,7 @@ 
Source code for src.problems.single_objective.continuous.schaffer2
    """
 
 

-[docs]
+[docs]
     def f(self, X: list) -> float:
         """
         Calculate the Modified Schaffer function #2 value for a given list of variables.
diff --git a/_modules/src/problems/single_objective/continuous/schwefel.html b/_modules/pycellga/problems/single_objective/continuous/schwefel.html
similarity index 91%
rename from _modules/src/problems/single_objective/continuous/schwefel.html
rename to _modules/pycellga/problems/single_objective/continuous/schwefel.html
index 073768f..2230abf 100644
--- a/_modules/src/problems/single_objective/continuous/schwefel.html
+++ b/_modules/pycellga/problems/single_objective/continuous/schwefel.html
@@ -3,7 +3,7 @@
 
   
   
-  src.problems.single_objective.continuous.schwefel — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.problems.single_objective.continuous.schwefel — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.problems.single_objective.continuous.schwefel
    • 
+      
  • pycellga.problems.single_objective.continuous.schwefel
    • 
       
  • 
       
    • 
   

@@ -70,12 +70,12 @@
           

            

              
-  
Source code for src.problems.single_objective.continuous.schwefel
+  
Source code for pycellga.problems.single_objective.continuous.schwefel
 from numpy import sin, sqrt
 from problems.abstract_problem import AbstractProblem
 
 

-[docs]
+[docs]
 class Schwefel(AbstractProblem):
     """
     Schwefel function implementation for optimization problems.
@@ -99,7 +99,7 @@ 
Source code for src.problems.single_objective.continuous.schwefel
    """
 
 

-[docs]
+[docs]
     def f(self, x: list) -> float:
         """
         Calculate the Schwefel function value for a given list of variables.
diff --git a/_modules/src/problems/single_objective/continuous/sphere.html b/_modules/pycellga/problems/single_objective/continuous/sphere.html
similarity index 90%
rename from _modules/src/problems/single_objective/continuous/sphere.html
rename to _modules/pycellga/problems/single_objective/continuous/sphere.html
index 461a06b..7bb1dbd 100644
--- a/_modules/src/problems/single_objective/continuous/sphere.html
+++ b/_modules/pycellga/problems/single_objective/continuous/sphere.html
@@ -3,7 +3,7 @@
 
   
   
-  src.problems.single_objective.continuous.sphere — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.problems.single_objective.continuous.sphere — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.problems.single_objective.continuous.sphere
    • 
+      
  • pycellga.problems.single_objective.continuous.sphere
    • 
       
  • 
       
    • 
   

@@ -70,11 +70,11 @@
           

            

              
-  
Source code for src.problems.single_objective.continuous.sphere
+  
Source code for pycellga.problems.single_objective.continuous.sphere
 from problems.abstract_problem import AbstractProblem
 
 

-[docs]
+[docs]
 class Sphere(AbstractProblem):
     """
     Sphere function implementation for optimization problems.
@@ -98,7 +98,7 @@ 
Source code for src.problems.single_objective.continuous.sphere
    """
 
 

-[docs]
+[docs]
     def f(self, x: list) -> float:
         """
         Calculate the Sphere function value for a given list of variables.
diff --git a/_modules/src/problems/single_objective/continuous/styblinskitang.html b/_modules/pycellga/problems/single_objective/continuous/styblinskitang.html
similarity index 91%
rename from _modules/src/problems/single_objective/continuous/styblinskitang.html
rename to _modules/pycellga/problems/single_objective/continuous/styblinskitang.html
index 48f1241..bbab8a8 100644
--- a/_modules/src/problems/single_objective/continuous/styblinskitang.html
+++ b/_modules/pycellga/problems/single_objective/continuous/styblinskitang.html
@@ -3,7 +3,7 @@
 
   
   
-  src.problems.single_objective.continuous.styblinskitang — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.problems.single_objective.continuous.styblinskitang — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.problems.single_objective.continuous.styblinskitang
    • 
+      
  • pycellga.problems.single_objective.continuous.styblinskitang
    • 
       
  • 
       
    • 
   

@@ -70,12 +70,12 @@
           

            

              
-  
Source code for src.problems.single_objective.continuous.styblinskitang
+  
Source code for pycellga.problems.single_objective.continuous.styblinskitang
 from problems.abstract_problem import AbstractProblem
 from mpmath import power as pw
 
 

-[docs]
+[docs]
 class StyblinskiTang(AbstractProblem):
     """
     Styblinski-Tang function implementation for optimization problems.
@@ -99,7 +99,7 @@ 
Source code for src.problems.single_objective.continuous.styblinskitang

     """
 
 

-[docs]
+[docs]
     def f(self, x: list) -> float:
         """
         Calculate the Styblinski-Tang function value for a given list of variables.
diff --git a/_modules/src/problems/single_objective/continuous/sumofdifferentpowers.html b/_modules/pycellga/problems/single_objective/continuous/sumofdifferentpowers.html
similarity index 89%
rename from _modules/src/problems/single_objective/continuous/sumofdifferentpowers.html
rename to _modules/pycellga/problems/single_objective/continuous/sumofdifferentpowers.html
index af5c651..0a0e942 100644
--- a/_modules/src/problems/single_objective/continuous/sumofdifferentpowers.html
+++ b/_modules/pycellga/problems/single_objective/continuous/sumofdifferentpowers.html
@@ -3,7 +3,7 @@
 
   
   
-  src.problems.single_objective.continuous.sumofdifferentpowers — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.problems.single_objective.continuous.sumofdifferentpowers — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.problems.single_objective.continuous.sumofdifferentpowers
    • 
+      
  • pycellga.problems.single_objective.continuous.sumofdifferentpowers
    • 
       
  • 
       
    • 
   

@@ -70,20 +70,20 @@
           

            

              
-  
Source code for src.problems.single_objective.continuous.sumofdifferentpowers
+  
Source code for pycellga.problems.single_objective.continuous.sumofdifferentpowers
 import numpy as np
 from mpmath import power as pw
 from problems.abstract_problem import AbstractProblem
 
 

-[docs]
+[docs]
 class Sumofdifferentpowers(AbstractProblem):
     """
     Sum of Different Powers function implementation for optimization problems.
     """
 
 

-[docs]
+[docs]
     def f(self, x: list) -> float:
         """
         Calculate the Sum of Different Powers function value for a given list of variables.
diff --git a/_modules/src/problems/single_objective/continuous/threehumps.html b/_modules/pycellga/problems/single_objective/continuous/threehumps.html
similarity index 92%
rename from _modules/src/problems/single_objective/continuous/threehumps.html
rename to _modules/pycellga/problems/single_objective/continuous/threehumps.html
index 84bc5b0..fb6469a 100644
--- a/_modules/src/problems/single_objective/continuous/threehumps.html
+++ b/_modules/pycellga/problems/single_objective/continuous/threehumps.html
@@ -3,7 +3,7 @@
 
   
   
-  src.problems.single_objective.continuous.threehumps — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.problems.single_objective.continuous.threehumps — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.problems.single_objective.continuous.threehumps
    • 
+      
  • pycellga.problems.single_objective.continuous.threehumps
    • 
       
  • 
       
    • 
   

@@ -70,12 +70,12 @@
           

            

              
-  
Source code for src.problems.single_objective.continuous.threehumps
+  
Source code for pycellga.problems.single_objective.continuous.threehumps
 from problems.abstract_problem import AbstractProblem
 from mpmath import power as pw
 
 

-[docs]
+[docs]
 class Threehumps(AbstractProblem):
     """
     Three Hump Camel function implementation for optimization problems.
@@ -99,7 +99,7 @@ 
Source code for src.problems.single_objective.continuous.threehumps
    """
 
 

-[docs]
+[docs]
     def f(self, x: list) -> float:
         """
         Calculate the Three Hump Camel function value for a given list of variables.
diff --git a/_modules/src/problems/single_objective/continuous/zakharov.html b/_modules/pycellga/problems/single_objective/continuous/zakharov.html
similarity index 93%
rename from _modules/src/problems/single_objective/continuous/zakharov.html
rename to _modules/pycellga/problems/single_objective/continuous/zakharov.html
index 7c88c13..20cc0fe 100644
--- a/_modules/src/problems/single_objective/continuous/zakharov.html
+++ b/_modules/pycellga/problems/single_objective/continuous/zakharov.html
@@ -3,7 +3,7 @@
 
   
   
-  src.problems.single_objective.continuous.zakharov — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.problems.single_objective.continuous.zakharov — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.problems.single_objective.continuous.zakharov
    • 
+      
  • pycellga.problems.single_objective.continuous.zakharov
    • 
       
  • 
       
    • 
   

@@ -70,11 +70,11 @@
           

            

              
-  
Source code for src.problems.single_objective.continuous.zakharov
+  
Source code for pycellga.problems.single_objective.continuous.zakharov
 from problems.abstract_problem import AbstractProblem
 from mpmath import power as pw
 

-[docs]
+[docs]
 class Zakharov(AbstractProblem):
     """
     Zakharov function implementation for optimization problems.
@@ -98,7 +98,7 @@ 
Source code for src.problems.single_objective.continuous.zakharov
    """
 
 

-[docs]
+[docs]
     def f(self, x: list) -> float:
         """
         Calculate the Zakharov function value for a given list of variables.
diff --git a/_modules/src/problems/single_objective/continuous/zettle.html b/_modules/pycellga/problems/single_objective/continuous/zettle.html
similarity index 92%
rename from _modules/src/problems/single_objective/continuous/zettle.html
rename to _modules/pycellga/problems/single_objective/continuous/zettle.html
index fd856a0..1d1a468 100644
--- a/_modules/src/problems/single_objective/continuous/zettle.html
+++ b/_modules/pycellga/problems/single_objective/continuous/zettle.html
@@ -3,7 +3,7 @@
 
   
   
-  src.problems.single_objective.continuous.zettle — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.problems.single_objective.continuous.zettle — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.problems.single_objective.continuous.zettle
    • 
+      
  • pycellga.problems.single_objective.continuous.zettle
    • 
       
  • 
       
    • 
   

@@ -70,12 +70,12 @@
           

            

              
-  
Source code for src.problems.single_objective.continuous.zettle
+  
Source code for pycellga.problems.single_objective.continuous.zettle
 from problems.abstract_problem import AbstractProblem
 from mpmath import power as pw
 
 

-[docs]
+[docs]
 class Zettle(AbstractProblem):
     """
     Zettle function implementation for optimization problems.
@@ -99,7 +99,7 @@ 
Source code for src.problems.single_objective.continuous.zettle
    """
 
 

-[docs]
+[docs]
     def f(self, x: list) -> float:
         """
         Calculate the Zettle function value for a given list of variables.
diff --git a/_modules/src/problems/single_objective/discrete/binary/count_sat.html b/_modules/pycellga/problems/single_objective/discrete/binary/count_sat.html
similarity index 92%
rename from _modules/src/problems/single_objective/discrete/binary/count_sat.html
rename to _modules/pycellga/problems/single_objective/discrete/binary/count_sat.html
index 561370d..d81536a 100644
--- a/_modules/src/problems/single_objective/discrete/binary/count_sat.html
+++ b/_modules/pycellga/problems/single_objective/discrete/binary/count_sat.html
@@ -3,7 +3,7 @@
 
   
   
-  src.problems.single_objective.discrete.binary.count_sat — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.problems.single_objective.discrete.binary.count_sat — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.problems.single_objective.discrete.binary.count_sat
    • 
+      
  • pycellga.problems.single_objective.discrete.binary.count_sat
    • 
       
  • 
       
    • 
   

@@ -70,10 +70,10 @@
           

            

              
-  
Source code for src.problems.single_objective.discrete.binary.count_sat
+  
Source code for pycellga.problems.single_objective.discrete.binary.count_sat
 from problems.abstract_problem import AbstractProblem
 

-[docs]
+[docs]
 class CountSat(AbstractProblem):
     """
     CountSat function implementation for optimization problems.
@@ -97,7 +97,7 @@ 
Source code for src.problems.single_objective.discrete.binary.count_sat

     """
 
 

-[docs]
+[docs]
     def f(self, x: list) -> float:
         """
         Calculate the CountSat function value for a given list of variables.
diff --git a/_modules/src/problems/single_objective/discrete/binary/ecc.html b/_modules/pycellga/problems/single_objective/discrete/binary/ecc.html
similarity index 93%
rename from _modules/src/problems/single_objective/discrete/binary/ecc.html
rename to _modules/pycellga/problems/single_objective/discrete/binary/ecc.html
index a378ddb..1b0d0a5 100644
--- a/_modules/src/problems/single_objective/discrete/binary/ecc.html
+++ b/_modules/pycellga/problems/single_objective/discrete/binary/ecc.html
@@ -3,7 +3,7 @@
 
   
   
-  src.problems.single_objective.discrete.binary.ecc — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.problems.single_objective.discrete.binary.ecc — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.problems.single_objective.discrete.binary.ecc
    • 
+      
  • pycellga.problems.single_objective.discrete.binary.ecc
    • 
       
  • 
       
    • 
   

@@ -70,11 +70,11 @@
           

            

              
-  
Source code for src.problems.single_objective.discrete.binary.ecc
+  
Source code for pycellga.problems.single_objective.discrete.binary.ecc
 from problems.abstract_problem import AbstractProblem
 
 

-[docs]
+[docs]
 class Ecc(AbstractProblem):
     """
     Error Correcting Codes Design Problem (ECC) function implementation for optimization problems.
@@ -98,7 +98,7 @@ 
Source code for src.problems.single_objective.discrete.binary.ecc
    """
 
 

-[docs]
+[docs]
     def f(self, x: list) -> float:
         """
         Calculate the ECC function value for a given list of variables.
diff --git a/_modules/src/problems/single_objective/discrete/binary/fms.html b/_modules/pycellga/problems/single_objective/discrete/binary/fms.html
similarity index 96%
rename from _modules/src/problems/single_objective/discrete/binary/fms.html
rename to _modules/pycellga/problems/single_objective/discrete/binary/fms.html
index 313d65f..6fdbb93 100644
--- a/_modules/src/problems/single_objective/discrete/binary/fms.html
+++ b/_modules/pycellga/problems/single_objective/discrete/binary/fms.html
@@ -3,7 +3,7 @@
 
   
   
-  src.problems.single_objective.discrete.binary.fms — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.problems.single_objective.discrete.binary.fms — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.problems.single_objective.discrete.binary.fms
    • 
+      
  • pycellga.problems.single_objective.discrete.binary.fms
    • 
       
  • 
       
    • 
   

@@ -70,11 +70,11 @@
           

            

              
-  
Source code for src.problems.single_objective.discrete.binary.fms
+  
Source code for pycellga.problems.single_objective.discrete.binary.fms
 from problems.abstract_problem import AbstractProblem
 from numpy import pi, sin, random
 

-[docs]
+[docs]
 class Fms(AbstractProblem):
     """
     Frequency Modulation Sound (FMS) function implementation for optimization problems.
@@ -98,7 +98,7 @@ 
Source code for src.problems.single_objective.discrete.binary.fms
    """
 
 

-[docs]
+[docs]
     def f(self, x: list) -> float:
         """
         Calculate the FMS function value for a given list of variables.
diff --git a/_modules/src/problems/single_objective/discrete/binary/maxcut100.html b/_modules/pycellga/problems/single_objective/discrete/binary/maxcut100.html
similarity index 99%
rename from _modules/src/problems/single_objective/discrete/binary/maxcut100.html
rename to _modules/pycellga/problems/single_objective/discrete/binary/maxcut100.html
index 6fc2f07..dd74dba 100644
--- a/_modules/src/problems/single_objective/discrete/binary/maxcut100.html
+++ b/_modules/pycellga/problems/single_objective/discrete/binary/maxcut100.html
@@ -3,7 +3,7 @@
 
   
   
-  src.problems.single_objective.discrete.binary.maxcut100 — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.problems.single_objective.discrete.binary.maxcut100 — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.problems.single_objective.discrete.binary.maxcut100
    • 
+      
  • pycellga.problems.single_objective.discrete.binary.maxcut100
    • 
       
  • 
       
    • 
   

@@ -70,11 +70,11 @@
           

            

              
-  
Source code for src.problems.single_objective.discrete.binary.maxcut100
+  
Source code for pycellga.problems.single_objective.discrete.binary.maxcut100
 from problems.abstract_problem import AbstractProblem
 
 

-[docs]
+[docs]
 class Maxcut100(AbstractProblem):
     """
     A class used to represent the Maximum Cut (MAXCUT) function for 100 nodes.
@@ -94,7 +94,7 @@ 
Source code for src.problems.single_objective.discrete.binary.maxcut100

     Maximum Fitness Value = 1077.0
     """
 

-[docs]
+[docs]
     def f(self, x: list) -> float:
         """
         Calculates the fitness value of a given chromosome for the Maxcut problem.
diff --git a/_modules/src/problems/single_objective/discrete/binary/maxcut20_01.html b/_modules/pycellga/problems/single_objective/discrete/binary/maxcut20_01.html
similarity index 97%
rename from _modules/src/problems/single_objective/discrete/binary/maxcut20_01.html
rename to _modules/pycellga/problems/single_objective/discrete/binary/maxcut20_01.html
index 69fce07..c34c5b1 100644
--- a/_modules/src/problems/single_objective/discrete/binary/maxcut20_01.html
+++ b/_modules/pycellga/problems/single_objective/discrete/binary/maxcut20_01.html
@@ -3,7 +3,7 @@
 
   
   
-  src.problems.single_objective.discrete.binary.maxcut20_01 — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.problems.single_objective.discrete.binary.maxcut20_01 — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.problems.single_objective.discrete.binary.maxcut20_01
    • 
+      
  • pycellga.problems.single_objective.discrete.binary.maxcut20_01
    • 
       
  • 
       
    • 
   

@@ -70,10 +70,10 @@
           

            

              
-  
Source code for src.problems.single_objective.discrete.binary.maxcut20_01
+  
Source code for pycellga.problems.single_objective.discrete.binary.maxcut20_01
 from problems.abstract_problem import AbstractProblem
 

-[docs]
+[docs]
 class Maxcut20_01(AbstractProblem):
     """
     Maximum Cut (MAXCUT) function implementation for optimization problems.
@@ -96,7 +96,7 @@ 
Source code for src.problems.single_objective.discrete.binary.maxcut20_01    """
 
 

-[docs]
+[docs]
     def f(self, x: list) -> float:
         """
         Calculate the MAXCUT function value for a given list of variables.
diff --git a/_modules/src/problems/single_objective/discrete/binary/maxcut20_09.html b/_modules/pycellga/problems/single_objective/discrete/binary/maxcut20_09.html
similarity index 97%
rename from _modules/src/problems/single_objective/discrete/binary/maxcut20_09.html
rename to _modules/pycellga/problems/single_objective/discrete/binary/maxcut20_09.html
index e3f8ba3..9792e28 100644
--- a/_modules/src/problems/single_objective/discrete/binary/maxcut20_09.html
+++ b/_modules/pycellga/problems/single_objective/discrete/binary/maxcut20_09.html
@@ -3,7 +3,7 @@
 
   
   
-  src.problems.single_objective.discrete.binary.maxcut20_09 — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.problems.single_objective.discrete.binary.maxcut20_09 — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.problems.single_objective.discrete.binary.maxcut20_09
    • 
+      
  • pycellga.problems.single_objective.discrete.binary.maxcut20_09
    • 
       
  • 
       
    • 
   

@@ -70,10 +70,10 @@
           

            

              
-  
Source code for src.problems.single_objective.discrete.binary.maxcut20_09
+  
Source code for pycellga.problems.single_objective.discrete.binary.maxcut20_09
 from problems.abstract_problem import AbstractProblem
 

-[docs]
+[docs]
 class Maxcut20_09(AbstractProblem):
     """
     Maximum Cut (MAXCUT) function implementation for optimization problems.
@@ -96,7 +96,7 @@ 
Source code for src.problems.single_objective.discrete.binary.maxcut20_09    """
 
 

-[docs]
+[docs]
     def f(self, x: list) -> float:
         """
         Calculate the MAXCUT function value for a given list of variables.
diff --git a/_modules/src/problems/single_objective/discrete/binary/mmdp.html b/_modules/pycellga/problems/single_objective/discrete/binary/mmdp.html
similarity index 93%
rename from _modules/src/problems/single_objective/discrete/binary/mmdp.html
rename to _modules/pycellga/problems/single_objective/discrete/binary/mmdp.html
index 262cc66..fb676cf 100644
--- a/_modules/src/problems/single_objective/discrete/binary/mmdp.html
+++ b/_modules/pycellga/problems/single_objective/discrete/binary/mmdp.html
@@ -3,7 +3,7 @@
 
   
   
-  src.problems.single_objective.discrete.binary.mmdp — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.problems.single_objective.discrete.binary.mmdp — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.problems.single_objective.discrete.binary.mmdp
    • 
+      
  • pycellga.problems.single_objective.discrete.binary.mmdp
    • 
       
  • 
       
    • 
   

@@ -70,11 +70,11 @@
           

            

              
-  
Source code for src.problems.single_objective.discrete.binary.mmdp
+  
Source code for pycellga.problems.single_objective.discrete.binary.mmdp
 from problems.abstract_problem import AbstractProblem
 
 

-[docs]
+[docs]
 class Mmdp(AbstractProblem):
     """
     Represents the Massively Multimodal Deceptive Problem (MMDP).
@@ -99,7 +99,7 @@ 
Source code for src.problems.single_objective.discrete.binary.mmdp
    """
 
 

-[docs]
+[docs]
     def f(self, x: list) -> float:
         """
         Evaluates the fitness of a given chromosome for the MMDP.
diff --git a/_modules/src/problems/single_objective/discrete/binary/one_max.html b/_modules/pycellga/problems/single_objective/discrete/binary/one_max.html
similarity index 89%
rename from _modules/src/problems/single_objective/discrete/binary/one_max.html
rename to _modules/pycellga/problems/single_objective/discrete/binary/one_max.html
index 2a22b9d..afc8963 100644
--- a/_modules/src/problems/single_objective/discrete/binary/one_max.html
+++ b/_modules/pycellga/problems/single_objective/discrete/binary/one_max.html
@@ -3,7 +3,7 @@
 
   
   
-  src.problems.single_objective.discrete.binary.one_max — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.problems.single_objective.discrete.binary.one_max — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.problems.single_objective.discrete.binary.one_max
    • 
+      
  • pycellga.problems.single_objective.discrete.binary.one_max
    • 
       
  • 
       
    • 
   

@@ -70,11 +70,11 @@
           

            

              
-  
Source code for src.problems.single_objective.discrete.binary.one_max
+  
Source code for pycellga.problems.single_objective.discrete.binary.one_max
 from problems.abstract_problem import AbstractProblem
 
 

-[docs]
+[docs]
 class OneMax(AbstractProblem):
     """
     Represents the OneMax problem.
@@ -93,7 +93,7 @@ 
Source code for src.problems.single_objective.discrete.binary.one_max
    """
 
 

-[docs]
+[docs]
     def f(self, x) -> float:
         """
         Evaluates the fitness of a given chromosome for the OneMax problem.
diff --git a/_modules/src/problems/single_objective/discrete/binary/peak.html b/_modules/pycellga/problems/single_objective/discrete/binary/peak.html
similarity index 93%
rename from _modules/src/problems/single_objective/discrete/binary/peak.html
rename to _modules/pycellga/problems/single_objective/discrete/binary/peak.html
index 8a2e3c5..71f334a 100644
--- a/_modules/src/problems/single_objective/discrete/binary/peak.html
+++ b/_modules/pycellga/problems/single_objective/discrete/binary/peak.html
@@ -3,7 +3,7 @@
 
   
   
-  src.problems.single_objective.discrete.binary.peak — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.problems.single_objective.discrete.binary.peak — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.problems.single_objective.discrete.binary.peak
    • 
+      
  • pycellga.problems.single_objective.discrete.binary.peak
    • 
       
  • 
       
    • 
   

@@ -70,12 +70,12 @@
           

            

              
-  
Source code for src.problems.single_objective.discrete.binary.peak
+  
Source code for pycellga.problems.single_objective.discrete.binary.peak
 from problems.abstract_problem import AbstractProblem
 from numpy import random
 
 

-[docs]
+[docs]
 class Peak(AbstractProblem):
     """
     Represents the Peak problem.
@@ -99,7 +99,7 @@ 
Source code for src.problems.single_objective.discrete.binary.peak
    """
 
 

-[docs]
+[docs]
     def f(self, x: list) -> float:
         """
         Evaluates the fitness of a given chromosome for the Peak problem.
diff --git a/_modules/src/problems/single_objective/discrete/permutation/tsp.html b/_modules/pycellga/problems/single_objective/discrete/permutation/tsp.html
similarity index 93%
rename from _modules/src/problems/single_objective/discrete/permutation/tsp.html
rename to _modules/pycellga/problems/single_objective/discrete/permutation/tsp.html
index 8a080d0..e8e01ac 100644
--- a/_modules/src/problems/single_objective/discrete/permutation/tsp.html
+++ b/_modules/pycellga/problems/single_objective/discrete/permutation/tsp.html
@@ -3,7 +3,7 @@
 
   
   
-  src.problems.single_objective.discrete.permutation.tsp — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.problems.single_objective.discrete.permutation.tsp — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.problems.single_objective.discrete.permutation.tsp
    • 
+      
  • pycellga.problems.single_objective.discrete.permutation.tsp
    • 
       
  • 
       
    • 
   

@@ -70,7 +70,7 @@
           

            

              
-  
Source code for src.problems.single_objective.discrete.permutation.tsp
+  
Source code for pycellga.problems.single_objective.discrete.permutation.tsp
 from problems.abstract_problem import AbstractProblem
 import tsplib95
 from math import sqrt
@@ -78,7 +78,7 @@ 
Source code for src.problems.single_objective.discrete.permutation.tsp
<
 import os
 
 

-[docs]
+[docs]
 class Tsp(AbstractProblem):
     """
     Represents the Traveling Salesman Problem (TSP).
@@ -96,7 +96,7 @@ 
Source code for src.problems.single_objective.discrete.permutation.tsp
<
     """
 
 

-[docs]
+[docs]
     def f(self, x: list) -> float:
         """
         Evaluates the fitness of a given chromosome (route) for the TSP.
@@ -161,7 +161,7 @@ 
Source code for src.problems.single_objective.discrete.permutation.tsp
<
 
 
 

-[docs]
+[docs]
     def euclidean_dist(self, a: list, b: list) -> float:
         """
         Computes the Euclidean distance between two nodes.
@@ -183,7 +183,7 @@ 
Source code for src.problems.single_objective.discrete.permutation.tsp
<
 
 
 

-[docs]
+[docs]
     def gographical_dist(self, a: list, b: list) -> float:
         """
         Computes the geographical distance between two nodes using the geodesic distance.
diff --git a/_modules/src/recombination/arithmetic_crossover.html b/_modules/pycellga/recombination/arithmetic_crossover.html
similarity index 94%
rename from _modules/src/recombination/arithmetic_crossover.html
rename to _modules/pycellga/recombination/arithmetic_crossover.html
index 84de5e7..404ae01 100644
--- a/_modules/src/recombination/arithmetic_crossover.html
+++ b/_modules/pycellga/recombination/arithmetic_crossover.html
@@ -3,7 +3,7 @@
 
   
   
-  src.recombination.arithmetic_crossover — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.recombination.arithmetic_crossover — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.recombination.arithmetic_crossover
    • 
+      
  • pycellga.recombination.arithmetic_crossover
    • 
       
  • 
       
    • 
   

@@ -70,7 +70,7 @@
           

            

              
-  
Source code for src.recombination.arithmetic_crossover
+  
Source code for pycellga.recombination.arithmetic_crossover
 import random
 from individual import *
 from problems.abstract_problem import AbstractProblem
@@ -78,7 +78,7 @@ 
Source code for src.recombination.arithmetic_crossover
from recombination.recombination_operator import RecombinationOperator
 
 

-[docs]
+[docs]
 class ArithmeticCrossover(RecombinationOperator):
     """
     ArithmeticCrossover performs an arithmetic crossover operation on a pair of parent individuals
@@ -93,7 +93,7 @@ 
Source code for src.recombination.arithmetic_crossover
    """
 
 

-[docs]
+[docs]
     def __init__(self, parents: list, problem: AbstractProblem):
         """
         Initialize the ArithmeticCrossover object.
@@ -110,7 +110,7 @@ 
Source code for src.recombination.arithmetic_crossover

-[docs]
+[docs]
     def get_recombinations(self) -> List[Individual]:
         """
         Perform the arithmetic crossover on the parent individuals to produce offspring.
diff --git a/_modules/src/recombination/blxalpha_crossover.html b/_modules/pycellga/recombination/blxalpha_crossover.html
similarity index 94%
rename from _modules/src/recombination/blxalpha_crossover.html
rename to _modules/pycellga/recombination/blxalpha_crossover.html
index b4c4d86..a749f15 100644
--- a/_modules/src/recombination/blxalpha_crossover.html
+++ b/_modules/pycellga/recombination/blxalpha_crossover.html
@@ -3,7 +3,7 @@
 
   
   
-  src.recombination.blxalpha_crossover — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.recombination.blxalpha_crossover — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.recombination.blxalpha_crossover
    • 
+      
  • pycellga.recombination.blxalpha_crossover
    • 
       
  • 
       
    • 
   

@@ -70,7 +70,7 @@
           

            

              
-  
Source code for src.recombination.blxalpha_crossover
+  
Source code for pycellga.recombination.blxalpha_crossover
 import random
 from individual import *
 from problems.abstract_problem import AbstractProblem
@@ -78,7 +78,7 @@ 
Source code for src.recombination.blxalpha_crossover
from recombination.recombination_operator import RecombinationOperator
 
 

-[docs]
+[docs]
 class BlxalphaCrossover(RecombinationOperator):
     """
     BlxalphaCrossover performs BLX-alpha crossover on a pair of parent individuals
@@ -93,7 +93,7 @@ 
Source code for src.recombination.blxalpha_crossover
    """
 
 

-[docs]
+[docs]
     def __init__(self, parents: list, problem: AbstractProblem):
         """
         Initialize the BlxalphaCrossover object.
@@ -110,7 +110,7 @@ 
Source code for src.recombination.blxalpha_crossover

-[docs]
+[docs]
     def combine(self, p1: Individual, p2: Individual, locationsource: Individual) -> Individual:
         """
         Combine two parent individuals using BLX-alpha crossover to produce a single offspring.
@@ -166,7 +166,7 @@ 
Source code for src.recombination.blxalpha_crossover

-[docs]
+[docs]
     def get_recombinations(self) -> List[Individual]:
         """
         Perform the BLX-alpha crossover on the parent individuals to produce offspring.
diff --git a/_modules/src/recombination/byte_one_point_crossover.html b/_modules/pycellga/recombination/byte_one_point_crossover.html
similarity index 95%
rename from _modules/src/recombination/byte_one_point_crossover.html
rename to _modules/pycellga/recombination/byte_one_point_crossover.html
index b619bfc..a3de532 100644
--- a/_modules/src/recombination/byte_one_point_crossover.html
+++ b/_modules/pycellga/recombination/byte_one_point_crossover.html
@@ -3,7 +3,7 @@
 
   
   
-  src.recombination.byte_one_point_crossover — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.recombination.byte_one_point_crossover — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.recombination.byte_one_point_crossover
    • 
+      
  • pycellga.recombination.byte_one_point_crossover
    • 
       
  • 
       
    • 
   

@@ -70,7 +70,7 @@
           

            

              
-  
Source code for src.recombination.byte_one_point_crossover
+  
Source code for pycellga.recombination.byte_one_point_crossover
 import numpy as np
 from individual import Individual
 from problems.abstract_problem import AbstractProblem
@@ -79,7 +79,7 @@ 
Source code for src.recombination.byte_one_point_crossover
from recombination.recombination_operator import RecombinationOperator
 
 

-[docs]
+[docs]
 class ByteOnePointCrossover(RecombinationOperator):
     """
     ByteOnePointCrossover operator defined in (Satman, 2013). ByteOnePointCrossover performs a 
@@ -94,7 +94,7 @@ 
Source code for src.recombination.byte_one_point_crossover
    """
 
 

-[docs]
+[docs]
     def __init__(self, parents: list, problem: AbstractProblem):
         """
         Initialize the ByteOnePointCrossover object.
@@ -111,7 +111,7 @@ 
Source code for src.recombination.byte_one_point_crossover

-[docs]
+[docs]
     def get_recombinations(self) -> List[Individual]:
         """
         Perform the one-point crossover on the parent individuals at the byte level to produce offspring.
diff --git a/_modules/src/recombination/byte_uniform_crossover.html b/_modules/pycellga/recombination/byte_uniform_crossover.html
similarity index 94%
rename from _modules/src/recombination/byte_uniform_crossover.html
rename to _modules/pycellga/recombination/byte_uniform_crossover.html
index dab1aec..1d574af 100644
--- a/_modules/src/recombination/byte_uniform_crossover.html
+++ b/_modules/pycellga/recombination/byte_uniform_crossover.html
@@ -3,7 +3,7 @@
 
   
   
-  src.recombination.byte_uniform_crossover — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.recombination.byte_uniform_crossover — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.recombination.byte_uniform_crossover
    • 
+      
  • pycellga.recombination.byte_uniform_crossover
    • 
       
  • 
       
    • 
   

@@ -70,7 +70,7 @@
           

            

              
-  
Source code for src.recombination.byte_uniform_crossover
+  
Source code for pycellga.recombination.byte_uniform_crossover
 import numpy.random as randomgenerator
 from individual import *
 from problems.abstract_problem import AbstractProblem
@@ -79,7 +79,7 @@ 
Source code for src.recombination.byte_uniform_crossover
from recombination.recombination_operator import RecombinationOperator
 
 

-[docs]
+[docs]
 class ByteUniformCrossover(RecombinationOperator):
     """
     ByteUniformCrossover operator defined in (Satman, 2013). ByteUniformCrossover performs a 
@@ -94,7 +94,7 @@ 
Source code for src.recombination.byte_uniform_crossover
    """
 
 

-[docs]
+[docs]
     def __init__(self, parents: list, problem: AbstractProblem):
         """
         Initialize the ByteUniformCrossover object.
@@ -111,7 +111,7 @@ 
Source code for src.recombination.byte_uniform_crossover

-[docs]
+[docs]
     def combine(self, p1: Individual, p2: Individual, locationsource: Individual) -> Individual:
         """
         Combine two parent individuals using uniform crossover at the byte level to produce a single offspring.
@@ -158,7 +158,7 @@ 
Source code for src.recombination.byte_uniform_crossover

-[docs]
+[docs]
     def get_recombinations(self) -> List[Individual]:
         """
         Perform the uniform crossover on the parent individuals to produce offspring.
diff --git a/_modules/src/recombination/flat_crossover.html b/_modules/pycellga/recombination/flat_crossover.html
similarity index 94%
rename from _modules/src/recombination/flat_crossover.html
rename to _modules/pycellga/recombination/flat_crossover.html
index 46d7c0e..e27d80d 100644
--- a/_modules/src/recombination/flat_crossover.html
+++ b/_modules/pycellga/recombination/flat_crossover.html
@@ -3,7 +3,7 @@
 
   
   
-  src.recombination.flat_crossover — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.recombination.flat_crossover — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.recombination.flat_crossover
    • 
+      
  • pycellga.recombination.flat_crossover
    • 
       
  • 
       
    • 
   

@@ -70,7 +70,7 @@
           

            

              
-  
Source code for src.recombination.flat_crossover
+  
Source code for pycellga.recombination.flat_crossover
 import random
 from individual import *
 from problems.abstract_problem import AbstractProblem
@@ -78,7 +78,7 @@ 
Source code for src.recombination.flat_crossover

 from recombination.recombination_operator import RecombinationOperator
 
 

-[docs]
+[docs]
 class FlatCrossover(RecombinationOperator):
     """
     FlatCrossover performs a flat crossover on a pair of parent individuals
@@ -93,7 +93,7 @@ 
Source code for src.recombination.flat_crossover

     """
 
 

-[docs]
+[docs]
     def __init__(self, parents: list, problem: AbstractProblem):
         """
         Initialize the FlatCrossover object.
@@ -110,7 +110,7 @@ 
Source code for src.recombination.flat_crossover

 
 
 

-[docs]
+[docs]
     def combine(self, p1: Individual, p2: Individual, locationsource: Individual) -> Individual:
         """
         Combine two parent individuals using flat crossover to produce a single offspring.
@@ -154,7 +154,7 @@ 
Source code for src.recombination.flat_crossover

 
 
 

-[docs]
+[docs]
     def get_recombinations(self) -> List[Individual]:
         """
         Perform the flat crossover on the parent individuals to produce offspring.
diff --git a/_modules/src/recombination/linear_crossover.html b/_modules/pycellga/recombination/linear_crossover.html
similarity index 94%
rename from _modules/src/recombination/linear_crossover.html
rename to _modules/pycellga/recombination/linear_crossover.html
index 076fb95..d3689cd 100644
--- a/_modules/src/recombination/linear_crossover.html
+++ b/_modules/pycellga/recombination/linear_crossover.html
@@ -3,7 +3,7 @@
 
   
   
-  src.recombination.linear_crossover — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.recombination.linear_crossover — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.recombination.linear_crossover
    • 
+      
  • pycellga.recombination.linear_crossover
    • 
       
  • 
       
    • 
   

@@ -70,7 +70,7 @@
           

            

              
-  
Source code for src.recombination.linear_crossover
+  
Source code for pycellga.recombination.linear_crossover
 import random
 from individual import *
 from problems.abstract_problem import AbstractProblem
@@ -78,7 +78,7 @@ 
Source code for src.recombination.linear_crossover
from recombination.recombination_operator import RecombinationOperator
 
 

-[docs]
+[docs]
 class LinearCrossover(RecombinationOperator):
     """
     LinearCrossover performs a linear crossover on a pair of parent individuals
@@ -93,7 +93,7 @@ 
Source code for src.recombination.linear_crossover
    """
 
 

-[docs]
+[docs]
     def __init__(self, parents: list, problem: AbstractProblem):
         """
         Initialize the LinearCrossover object.
@@ -110,7 +110,7 @@ 
Source code for src.recombination.linear_crossover

-[docs]
+[docs]
     def combine(self, p1: Individual, p2: Individual, locationsource: Individual) -> Individual:
         """
         Combine two parent individuals using linear crossover to produce a single offspring.
@@ -159,7 +159,7 @@ 
Source code for src.recombination.linear_crossover

-[docs]
+[docs]
     def get_recombinations(self) -> List[Individual]:
         """
         Perform the linear crossover on the parent individuals to produce offspring.
diff --git a/_modules/src/recombination/one_point_crossover.html b/_modules/pycellga/recombination/one_point_crossover.html
similarity index 94%
rename from _modules/src/recombination/one_point_crossover.html
rename to _modules/pycellga/recombination/one_point_crossover.html
index 6dba7bf..9003341 100644
--- a/_modules/src/recombination/one_point_crossover.html
+++ b/_modules/pycellga/recombination/one_point_crossover.html
@@ -3,7 +3,7 @@
 
   
   
-  src.recombination.one_point_crossover — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.recombination.one_point_crossover — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.recombination.one_point_crossover
    • 
+      
  • pycellga.recombination.one_point_crossover
    • 
       
  • 
       
    • 
   

@@ -70,7 +70,7 @@
           

            

              
-  
Source code for src.recombination.one_point_crossover
+  
Source code for pycellga.recombination.one_point_crossover
 import numpy as np
 from individual import *
 from problems.abstract_problem import AbstractProblem
@@ -79,7 +79,7 @@ 
Source code for src.recombination.one_point_crossover

-[docs]
+[docs]
 class OnePointCrossover(RecombinationOperator):
     """
     OnePointCrossover performs a one-point crossover on a pair of parent individuals
@@ -94,7 +94,7 @@ 
Source code for src.recombination.one_point_crossover
    """
 
 

-[docs]
+[docs]
     def __init__(self, parents: list, problem: AbstractProblem):
         """
         Initialize the OnePointCrossover object.
@@ -111,7 +111,7 @@ 
Source code for src.recombination.one_point_crossover

-[docs]
+[docs]
     def get_recombinations(self) -> List[Individual]:
         """
         Perform the one-point crossover on the parent individuals to produce offspring.
diff --git a/_modules/src/recombination/pmx_crossover.html b/_modules/pycellga/recombination/pmx_crossover.html
similarity index 96%
rename from _modules/src/recombination/pmx_crossover.html
rename to _modules/pycellga/recombination/pmx_crossover.html
index aeebf6e..e5c2095 100644
--- a/_modules/src/recombination/pmx_crossover.html
+++ b/_modules/pycellga/recombination/pmx_crossover.html
@@ -3,7 +3,7 @@
 
   
   
-  src.recombination.pmx_crossover — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.recombination.pmx_crossover — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.recombination.pmx_crossover
    • 
+      
  • pycellga.recombination.pmx_crossover
    • 
       
  • 
       
    • 
   

@@ -70,14 +70,14 @@
           

            

              
-  
Source code for src.recombination.pmx_crossover
+  
Source code for pycellga.recombination.pmx_crossover
 from individual import *
 from problems.abstract_problem import AbstractProblem
 from typing import List
 from recombination.recombination_operator import RecombinationOperator
 
 

-[docs]
+[docs]
 class PMXCrossover(RecombinationOperator):
     """
     PMXCrossover performs Partially Mapped Crossover (PMX) on a pair of parent individuals
@@ -92,7 +92,7 @@ 
Source code for src.recombination.pmx_crossover
<
     """
 
 

-[docs]
+[docs]
     def __init__(self, parents: list, problem: AbstractProblem):
         """
         Initialize the PMXCrossover object.
@@ -109,7 +109,7 @@ 
Source code for src.recombination.pmx_crossover
<
 
 
 

-[docs]
+[docs]
     def get_recombinations(self) -> List[Individual]:
         """
         Perform the PMX crossover on the parent individuals to produce offspring.
diff --git a/_modules/src/recombination/recombination_operator.html b/_modules/pycellga/recombination/recombination_operator.html
similarity index 86%
rename from _modules/src/recombination/recombination_operator.html
rename to _modules/pycellga/recombination/recombination_operator.html
index 21933f3..b10bc69 100644
--- a/_modules/src/recombination/recombination_operator.html
+++ b/_modules/pycellga/recombination/recombination_operator.html
@@ -3,7 +3,7 @@
 
   
   
-  src.recombination.recombination_operator — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.recombination.recombination_operator — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.recombination.recombination_operator
    • 
+      
  • pycellga.recombination.recombination_operator
    • 
       
  • 
       
    • 
   

@@ -70,12 +70,12 @@
           

            

              
-  
Source code for src.recombination.recombination_operator
+  
Source code for pycellga.recombination.recombination_operator
 

-[docs]
+[docs]
 class RecombinationOperator:
 

-[docs]
+[docs]
      def get_recombinations(self) -> list:
         pass

 

diff --git a/_modules/src/recombination/two_point_crossover.html b/_modules/pycellga/recombination/two_point_crossover.html
similarity index 95%
rename from _modules/src/recombination/two_point_crossover.html
rename to _modules/pycellga/recombination/two_point_crossover.html
index 4e9479f..bedd360 100644
--- a/_modules/src/recombination/two_point_crossover.html
+++ b/_modules/pycellga/recombination/two_point_crossover.html
@@ -3,7 +3,7 @@
 
   
   
-  src.recombination.two_point_crossover — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.recombination.two_point_crossover — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.recombination.two_point_crossover
    • 
+      
  • pycellga.recombination.two_point_crossover
    • 
       
  • 
       
    • 
   

@@ -70,7 +70,7 @@
           

            

              
-  
Source code for src.recombination.two_point_crossover
+  
Source code for pycellga.recombination.two_point_crossover
 import numpy as np
 from individual import Individual
 from problems.abstract_problem import AbstractProblem
@@ -78,7 +78,7 @@ 
Source code for src.recombination.two_point_crossover
from recombination.recombination_operator import RecombinationOperator
 
 

-[docs]
+[docs]
 class TwoPointCrossover(RecombinationOperator):
     """
     TwoPointCrossover performs a two-point crossover on a pair of parent individuals
@@ -93,7 +93,7 @@ 
Source code for src.recombination.two_point_crossover
    """
 
 

-[docs]
+[docs]
     def __init__(self, parents: list, problem: AbstractProblem):
         """
         Initialize the TwoPointCrossover object.
@@ -110,7 +110,7 @@ 
Source code for src.recombination.two_point_crossover

-[docs]
+[docs]
     def get_recombinations(self) -> List[Individual]:
         """
         Perform the two-point crossover on the parent individuals to produce offspring.
diff --git a/_modules/src/recombination/unfair_avarage_crossover.html b/_modules/pycellga/recombination/unfair_avarage_crossover.html
similarity index 95%
rename from _modules/src/recombination/unfair_avarage_crossover.html
rename to _modules/pycellga/recombination/unfair_avarage_crossover.html
index b3b7ee8..d756678 100644
--- a/_modules/src/recombination/unfair_avarage_crossover.html
+++ b/_modules/pycellga/recombination/unfair_avarage_crossover.html
@@ -3,7 +3,7 @@
 
   
   
-  src.recombination.unfair_avarage_crossover — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.recombination.unfair_avarage_crossover — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.recombination.unfair_avarage_crossover
    • 
+      
  • pycellga.recombination.unfair_avarage_crossover
    • 
       
  • 
       
    • 
   

@@ -70,7 +70,7 @@
           

            

              
-  
Source code for src.recombination.unfair_avarage_crossover
+  
Source code for pycellga.recombination.unfair_avarage_crossover
 import random
 from individual import *
 from problems.abstract_problem import AbstractProblem
@@ -78,7 +78,7 @@ 
Source code for src.recombination.unfair_avarage_crossover
from recombination.recombination_operator import RecombinationOperator
 
 

-[docs]
+[docs]
 class UnfairAvarageCrossover(RecombinationOperator):
     """
     UnfairAvarageCrossover performs an unfair average crossover on a pair of parent individuals
@@ -93,7 +93,7 @@ 
Source code for src.recombination.unfair_avarage_crossover
    """
 
 

-[docs]
+[docs]
     def __init__(self, parents: list, problem: AbstractProblem):
         """
         Initialize the UnfairAvarageCrossover object.
@@ -110,7 +110,7 @@ 
Source code for src.recombination.unfair_avarage_crossover

-[docs]
+[docs]
     def get_recombinations(self) -> List[Individual]:
         """
         Perform the unfair average crossover on the parent individuals to produce offspring.
diff --git a/_modules/src/recombination/uniform_crossover.html b/_modules/pycellga/recombination/uniform_crossover.html
similarity index 93%
rename from _modules/src/recombination/uniform_crossover.html
rename to _modules/pycellga/recombination/uniform_crossover.html
index 8ae9a9b..e9694d0 100644
--- a/_modules/src/recombination/uniform_crossover.html
+++ b/_modules/pycellga/recombination/uniform_crossover.html
@@ -3,7 +3,7 @@
 
   
   
-  src.recombination.uniform_crossover — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.recombination.uniform_crossover — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.recombination.uniform_crossover
    • 
+      
  • pycellga.recombination.uniform_crossover
    • 
       
  • 
       
    • 
   

@@ -70,7 +70,7 @@
           

            

              
-  
Source code for src.recombination.uniform_crossover
+  
Source code for pycellga.recombination.uniform_crossover
 import numpy.random as randomgenerator
 from individual import *
 from problems.abstract_problem import AbstractProblem
@@ -78,7 +78,7 @@ 
Source code for src.recombination.uniform_crossover
from recombination.recombination_operator import RecombinationOperator
 
 

-[docs]
+[docs]
 class UniformCrossover(RecombinationOperator):
     """
     UniformCrossover performs a uniform crossover on a pair of parent individuals
@@ -93,7 +93,7 @@ 
Source code for src.recombination.uniform_crossover
    """
 
 

-[docs]
+[docs]
     def __init__(self, parents: list, problem: AbstractProblem):
         """
         Initialize the UniformCrossover object.
@@ -110,7 +110,7 @@ 
Source code for src.recombination.uniform_crossover

-[docs]
+[docs]
     def combine(self, p1: Individual, p2: Individual, locationsource: Individual) -> Individual:
         """
         Combine two parent individuals using uniform crossover to produce a single offspring.
@@ -146,7 +146,7 @@ 
Source code for src.recombination.uniform_crossover

-[docs]
+[docs]
     def get_recombinations(self) -> List[Individual]:
         """
         Perform the uniform crossover on the parent individuals to produce offspring.
diff --git a/_modules/src/selection/roulette_wheel_selection.html b/_modules/pycellga/selection/roulette_wheel_selection.html
similarity index 93%
rename from _modules/src/selection/roulette_wheel_selection.html
rename to _modules/pycellga/selection/roulette_wheel_selection.html
index a016a37..d3793bd 100644
--- a/_modules/src/selection/roulette_wheel_selection.html
+++ b/_modules/pycellga/selection/roulette_wheel_selection.html
@@ -3,7 +3,7 @@
 
   
   
-  src.selection.roulette_wheel_selection — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.selection.roulette_wheel_selection — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.selection.roulette_wheel_selection
    • 
+      
  • pycellga.selection.roulette_wheel_selection
    • 
       
  • 
       
    • 
   

@@ -70,14 +70,14 @@
           

            

              
-  
Source code for src.selection.roulette_wheel_selection
+  
Source code for pycellga.selection.roulette_wheel_selection
 from typing import List
 from individual import Individual
 from selection.selection_operator import SelectionOperator
 import random
 
 

-[docs]
+[docs]
 class RouletteWheelSelection(SelectionOperator):
     """
     RouletteWheelSelection performs a roulette wheel selection on a population of individuals
@@ -92,7 +92,7 @@ 
Source code for src.selection.roulette_wheel_selection
    """
 
 

-[docs]
+[docs]
     def __init__(self, pop_list: List[Individual] = [], c: int = 0):
         """
         Initialize the RouletteWheelSelection object.
@@ -109,7 +109,7 @@ 
Source code for src.selection.roulette_wheel_selection

-[docs]
+[docs]
     def get_parents(self) -> List[Individual]:
         """
         Perform the roulette wheel selection to get parent individuals.
diff --git a/_modules/src/selection/selection_operator.html b/_modules/pycellga/selection/selection_operator.html
similarity index 87%
rename from _modules/src/selection/selection_operator.html
rename to _modules/pycellga/selection/selection_operator.html
index b36c200..b639d75 100644
--- a/_modules/src/selection/selection_operator.html
+++ b/_modules/pycellga/selection/selection_operator.html
@@ -3,7 +3,7 @@
 
   
   
-  src.selection.selection_operator — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.selection.selection_operator — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.selection.selection_operator
    • 
+      
  • pycellga.selection.selection_operator
    • 
       
  • 
       
    • 
   

@@ -70,13 +70,13 @@
           

            

              
-  
Source code for src.selection.selection_operator
+  
Source code for pycellga.selection.selection_operator
 

-[docs]
+[docs]
 class SelectionOperator:
     
 

-[docs]
+[docs]
     def get_parents(self) -> list:
         pass

 

diff --git a/_modules/src/selection/tournament_selection.html b/_modules/pycellga/selection/tournament_selection.html
similarity index 94%
rename from _modules/src/selection/tournament_selection.html
rename to _modules/pycellga/selection/tournament_selection.html
index fdf11a3..bf22944 100644
--- a/_modules/src/selection/tournament_selection.html
+++ b/_modules/pycellga/selection/tournament_selection.html
@@ -3,7 +3,7 @@
 
   
   
-  src.selection.tournament_selection — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.selection.tournament_selection — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.selection.tournament_selection
    • 
+      
  • pycellga.selection.tournament_selection
    • 
       
  • 
       
    • 
   

@@ -70,7 +70,7 @@
           

            

              
-  
Source code for src.selection.tournament_selection
+  
Source code for pycellga.selection.tournament_selection
 from typing import List
 from individual import Individual
 from selection.selection_operator import SelectionOperator
@@ -78,7 +78,7 @@ 
Source code for src.selection.tournament_selection

-[docs]
+[docs]
 class TournamentSelection(SelectionOperator):
     """
     TournamentSelection performs a tournament selection on a population of individuals
@@ -95,7 +95,7 @@ 
Source code for src.selection.tournament_selection
    """
 
 

-[docs]
+[docs]
     def __init__(self, pop_list: List[Individual] = [], c: int = 0, K: int = 2):
         """
         Initialize the TournamentSelection object.
@@ -115,7 +115,7 @@ 
Source code for src.selection.tournament_selection

-[docs]
+[docs]
     def get_parents(self) -> List[Individual]:
         """
         Perform the tournament selection to get parent individuals.
diff --git a/_modules/src/tests/test_ackley.html b/_modules/pycellga/tests/test_ackley.html
similarity index 95%
rename from _modules/src/tests/test_ackley.html
rename to _modules/pycellga/tests/test_ackley.html
index c219680..f8794bc 100644
--- a/_modules/src/tests/test_ackley.html
+++ b/_modules/pycellga/tests/test_ackley.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_ackley — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_ackley — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_ackley
    • 
+      
  • pycellga.tests.test_ackley
    • 
       
  • 
       
    • 
   

@@ -70,12 +70,12 @@
           

            

              
-  
Source code for src.tests.test_ackley
+  
Source code for pycellga.tests.test_ackley
 from problems.single_objective.continuous.ackley import Ackley
 import numpy as np
 
 

-[docs]
+[docs]
 def test_ackley():
     """
     Test the Ackley function implementation.
diff --git a/_modules/src/tests/test_arithmetic_crossover.html b/_modules/pycellga/tests/test_arithmetic_crossover.html
similarity index 94%
rename from _modules/src/tests/test_arithmetic_crossover.html
rename to _modules/pycellga/tests/test_arithmetic_crossover.html
index 06a5649..ca65a35 100644
--- a/_modules/src/tests/test_arithmetic_crossover.html
+++ b/_modules/pycellga/tests/test_arithmetic_crossover.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_arithmetic_crossover — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_arithmetic_crossover — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_arithmetic_crossover
    • 
+      
  • pycellga.tests.test_arithmetic_crossover
    • 
       
  • 
       
    • 
   

@@ -70,7 +70,7 @@
           

            

              
-  
Source code for src.tests.test_arithmetic_crossover
+  
Source code for pycellga.tests.test_arithmetic_crossover
 import pytest
 import random
 from individual import Individual, GeneType
@@ -78,13 +78,13 @@ 
Source code for src.tests.test_arithmetic_crossover
from recombination.arithmetic_crossover import ArithmeticCrossover  # Replace with the actual path if different
 
 

-[docs]
+[docs]
 class MockProblem(AbstractProblem):
     """
     A mock problem class for testing purposes.
     """
 

-[docs]
+[docs]
     def f(self, x: list) -> float:
         """
         A mock fitness function that simply sums the chromosome values.
@@ -104,7 +104,7 @@ 
Source code for src.tests.test_arithmetic_crossover

-[docs]
+[docs]
 @pytest.fixture
 def setup_parents():
     """
@@ -125,7 +125,7 @@ 
Source code for src.tests.test_arithmetic_crossover

-[docs]
+[docs]
 @pytest.fixture
 def setup_problem():
     """
@@ -140,7 +140,7 @@ 
Source code for src.tests.test_arithmetic_crossover

-[docs]
+[docs]
 def test_arithmetic_crossover(setup_parents, setup_problem):
     """
     Test the ArithmeticCrossover function implementation.
diff --git a/_modules/src/tests/test_bentcigar_function.html b/_modules/pycellga/tests/test_bentcigar_function.html
similarity index 94%
rename from _modules/src/tests/test_bentcigar_function.html
rename to _modules/pycellga/tests/test_bentcigar_function.html
index 723a364..ade32a5 100644
--- a/_modules/src/tests/test_bentcigar_function.html
+++ b/_modules/pycellga/tests/test_bentcigar_function.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_bentcigar_function — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_bentcigar_function — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_bentcigar_function
    • 
+      
  • pycellga.tests.test_bentcigar_function
    • 
       
  • 
       
    • 
   

@@ -70,12 +70,12 @@
           

            

              
-  
Source code for src.tests.test_bentcigar_function
+  
Source code for pycellga.tests.test_bentcigar_function
 import pytest
 from problems.single_objective.continuous.bentcigar import Bentcigar  # Replace with the actual path if different
 
 

-[docs]
+[docs]
 @pytest.fixture
 def setup_bentcigar():
     """
@@ -90,7 +90,7 @@ 
Source code for src.tests.test_bentcigar_function

-[docs]
+[docs]
 def test_bentcigar_function(setup_bentcigar):
     """
     Test the Bentcigar function implementation.
diff --git a/_modules/src/tests/test_bit_flip_mutation.html b/_modules/pycellga/tests/test_bit_flip_mutation.html
similarity index 96%
rename from _modules/src/tests/test_bit_flip_mutation.html
rename to _modules/pycellga/tests/test_bit_flip_mutation.html
index d1df9f3..daf7360 100644
--- a/_modules/src/tests/test_bit_flip_mutation.html
+++ b/_modules/pycellga/tests/test_bit_flip_mutation.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_bit_flip_mutation — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_bit_flip_mutation — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_bit_flip_mutation
    • 
+      
  • pycellga.tests.test_bit_flip_mutation
    • 
       
  • 
       
    • 
   

@@ -70,7 +70,7 @@
           

            

              
-  
Source code for src.tests.test_bit_flip_mutation
+  
Source code for pycellga.tests.test_bit_flip_mutation
 import numpy as np
 import pytest
 from mutation.bit_flip_mutation import BitFlipMutation
@@ -78,7 +78,7 @@ 
Source code for src.tests.test_bit_flip_mutation

 from individual import Individual, GeneType
 
 

-[docs]
+[docs]
 def test_bit_flip_mutation():
     """
     Test the BitFlipMutation class for the Individual class on the OneMax problem.
diff --git a/_modules/src/tests/test_blxalpha_crossover.html b/_modules/pycellga/tests/test_blxalpha_crossover.html
similarity index 94%
rename from _modules/src/tests/test_blxalpha_crossover.html
rename to _modules/pycellga/tests/test_blxalpha_crossover.html
index bf43113..2546c3c 100644
--- a/_modules/src/tests/test_blxalpha_crossover.html
+++ b/_modules/pycellga/tests/test_blxalpha_crossover.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_blxalpha_crossover — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_blxalpha_crossover — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_blxalpha_crossover
    • 
+      
  • pycellga.tests.test_blxalpha_crossover
    • 
       
  • 
       
    • 
   

@@ -70,7 +70,7 @@
           

            

              
-  
Source code for src.tests.test_blxalpha_crossover
+  
Source code for pycellga.tests.test_blxalpha_crossover
 import pytest
 import random
 from individual import Individual, GeneType
@@ -78,13 +78,13 @@ 
Source code for src.tests.test_blxalpha_crossover
from recombination.blxalpha_crossover import BlxalphaCrossover  # Replace with the actual path if different
 
 

-[docs]
+[docs]
 class MockProblem(AbstractProblem):
     """
     A mock problem class for testing purposes.
     """
 

-[docs]
+[docs]
     def f(self, x: list) -> float:
         """
         A mock fitness function that simply sums the chromosome values.
@@ -104,7 +104,7 @@ 
Source code for src.tests.test_blxalpha_crossover

-[docs]
+[docs]
 @pytest.fixture
 def setup_parents():
     """
@@ -125,7 +125,7 @@ 
Source code for src.tests.test_blxalpha_crossover

-[docs]
+[docs]
 @pytest.fixture
 def setup_problem():
     """
@@ -140,7 +140,7 @@ 
Source code for src.tests.test_blxalpha_crossover

-[docs]
+[docs]
 def test_blxalpha_crossover(setup_parents, setup_problem):
     """
     Test the BlxalphaCrossover function implementation.
diff --git a/_modules/src/tests/test_bohachevsky.html b/_modules/pycellga/tests/test_bohachevsky.html
similarity index 95%
rename from _modules/src/tests/test_bohachevsky.html
rename to _modules/pycellga/tests/test_bohachevsky.html
index afa08d9..14813f9 100644
--- a/_modules/src/tests/test_bohachevsky.html
+++ b/_modules/pycellga/tests/test_bohachevsky.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_bohachevsky — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_bohachevsky — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_bohachevsky
    • 
+      
  • pycellga.tests.test_bohachevsky
    • 
       
  • 
       
    • 
   

@@ -70,11 +70,11 @@
           

            

              
-  
Source code for src.tests.test_bohachevsky
+  
Source code for pycellga.tests.test_bohachevsky
 from problems.single_objective.continuous.bohachevsky import Bohachevsky
 
 

-[docs]
+[docs]
 def test_bohachevsky():
     """
     Test the Bohachevsky function implementation.
diff --git a/_modules/src/tests/test_byte_mutation.html b/_modules/pycellga/tests/test_byte_mutation.html
similarity index 93%
rename from _modules/src/tests/test_byte_mutation.html
rename to _modules/pycellga/tests/test_byte_mutation.html
index 2b7c17d..47e6fca 100644
--- a/_modules/src/tests/test_byte_mutation.html
+++ b/_modules/pycellga/tests/test_byte_mutation.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_byte_mutation — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_byte_mutation — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_byte_mutation
    • 
+      
  • pycellga.tests.test_byte_mutation
    • 
       
  • 
       
    • 
   

@@ -70,7 +70,7 @@
           

            

              
-  
Source code for src.tests.test_byte_mutation
+  
Source code for pycellga.tests.test_byte_mutation
 import pytest
 import numpy as np
 from individual import Individual, GeneType
@@ -79,13 +79,13 @@ 
Source code for src.tests.test_byte_mutation
-[docs]
+[docs]
 class MockProblem(AbstractProblem):
     """
     A mock problem class for testing purposes.
     """
 

-[docs]
+[docs]
     def f(self, x: list) -> float:
         """
         A mock fitness function that simply sums the chromosome values.
@@ -105,7 +105,7 @@ 
Source code for src.tests.test_byte_mutation
-[docs]
+[docs]
 @pytest.fixture
 def setup_individual():
     """
@@ -123,7 +123,7 @@ 
Source code for src.tests.test_byte_mutation
-[docs]
+[docs]
 @pytest.fixture
 def setup_problem():
     """
@@ -138,7 +138,7 @@ 
Source code for src.tests.test_byte_mutation
-[docs]
+[docs]
 def test_byte_mutation(setup_individual, setup_problem):
     """
     Test the ByteMutation function implementation.
diff --git a/_modules/src/tests/test_byte_mutation_random.html b/_modules/pycellga/tests/test_byte_mutation_random.html
similarity index 93%
rename from _modules/src/tests/test_byte_mutation_random.html
rename to _modules/pycellga/tests/test_byte_mutation_random.html
index 9cdcc88..5486b8d 100644
--- a/_modules/src/tests/test_byte_mutation_random.html
+++ b/_modules/pycellga/tests/test_byte_mutation_random.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_byte_mutation_random — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_byte_mutation_random — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_byte_mutation_random
    • 
+      
  • pycellga.tests.test_byte_mutation_random
    • 
       
  • 
       
    • 
   

@@ -70,7 +70,7 @@
           

            

              
-  
Source code for src.tests.test_byte_mutation_random
+  
Source code for pycellga.tests.test_byte_mutation_random
 import pytest
 import numpy as np
 from individual import Individual, GeneType
@@ -78,13 +78,13 @@ 
Source code for src.tests.test_byte_mutation_random
from mutation.byte_mutation_random import ByteMutationRandom  # Replace with the actual path if different
 
 

-[docs]
+[docs]
 class MockProblem(AbstractProblem):
     """
     A mock problem class for testing purposes.
     """
 

-[docs]
+[docs]
     def f(self, x: list) -> float:
         """
         A mock fitness function that simply sums the chromosome values.
@@ -104,7 +104,7 @@ 
Source code for src.tests.test_byte_mutation_random

-[docs]
+[docs]
 @pytest.fixture
 def setup_individual():
     """
@@ -122,7 +122,7 @@ 
Source code for src.tests.test_byte_mutation_random

-[docs]
+[docs]
 @pytest.fixture
 def setup_problem():
     """
@@ -137,7 +137,7 @@ 
Source code for src.tests.test_byte_mutation_random

-[docs]
+[docs]
 def test_byte_mutation_random(setup_individual, setup_problem):
     """
     Test the ByteMutationRandom function implementation.
diff --git a/_modules/src/tests/test_byte_one_point_crossover.html b/_modules/pycellga/tests/test_byte_one_point_crossover.html
similarity index 94%
rename from _modules/src/tests/test_byte_one_point_crossover.html
rename to _modules/pycellga/tests/test_byte_one_point_crossover.html
index e0fe484..d989dde 100644
--- a/_modules/src/tests/test_byte_one_point_crossover.html
+++ b/_modules/pycellga/tests/test_byte_one_point_crossover.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_byte_one_point_crossover — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_byte_one_point_crossover — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_byte_one_point_crossover
    • 
+      
  • pycellga.tests.test_byte_one_point_crossover
    • 
       
  • 
       
    • 
   

@@ -70,7 +70,7 @@
           

            

              
-  
Source code for src.tests.test_byte_one_point_crossover
+  
Source code for pycellga.tests.test_byte_one_point_crossover
 import pytest
 import numpy as np
 from individual import Individual, GeneType
@@ -79,13 +79,13 @@ 
Source code for src.tests.test_byte_one_point_crossover

-[docs]
+[docs]
 class MockProblem(AbstractProblem):
     """
     A mock problem class for testing purposes.
     """
 

-[docs]
+[docs]
     def f(self, x: list) -> float:
         """
         A mock fitness function that simply sums the chromosome values.
@@ -105,7 +105,7 @@ 
Source code for src.tests.test_byte_one_point_crossover

-[docs]
+[docs]
 @pytest.fixture
 def setup_parents():
     """
@@ -126,7 +126,7 @@ 
Source code for src.tests.test_byte_one_point_crossover

-[docs]
+[docs]
 @pytest.fixture
 def setup_problem():
     """
@@ -141,7 +141,7 @@ 
Source code for src.tests.test_byte_one_point_crossover

-[docs]
+[docs]
 def test_byte_one_point_crossover(setup_parents, setup_problem):
     """
     Test the ByteOnePointCrossover function implementation.
diff --git a/_modules/src/tests/test_byte_operators.html b/_modules/pycellga/tests/test_byte_operators.html
similarity index 94%
rename from _modules/src/tests/test_byte_operators.html
rename to _modules/pycellga/tests/test_byte_operators.html
index 10242fe..7c74159 100644
--- a/_modules/src/tests/test_byte_operators.html
+++ b/_modules/pycellga/tests/test_byte_operators.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_byte_operators — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_byte_operators — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
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  • Module code
    • 
-      
  • src.tests.test_byte_operators
    • 
+      
  • pycellga.tests.test_byte_operators
    • 
       
  • 
       
    • 
   

@@ -70,12 +70,12 @@
           

            

              
-  
Source code for src.tests.test_byte_operators
+  
Source code for pycellga.tests.test_byte_operators
 import pytest
 from byte_operators import float_to_bits, bits_to_float, floats_to_bits, bits_to_floats
 
 

-[docs]
+[docs]
 def test_float_to_bits():
     """
     Test the float_to_bits function.
@@ -88,7 +88,7 @@ 
Source code for src.tests.test_byte_operators

-[docs]
+[docs]
 def test_bits_to_float():
     """
     Test the bits_to_float function.
@@ -100,7 +100,7 @@ 
Source code for src.tests.test_byte_operators

-[docs]
+[docs]
 def test_floats_to_bits():
     """
     Test the floats_to_bits function.
@@ -112,7 +112,7 @@ 
Source code for src.tests.test_byte_operators

-[docs]
+[docs]
 def test_bits_to_floats():
     """
     Test the bits_to_floats function.
diff --git a/_modules/src/tests/test_byte_uniform_crossover.html b/_modules/pycellga/tests/test_byte_uniform_crossover.html
similarity index 94%
rename from _modules/src/tests/test_byte_uniform_crossover.html
rename to _modules/pycellga/tests/test_byte_uniform_crossover.html
index 82cc40c..9ce58b8 100644
--- a/_modules/src/tests/test_byte_uniform_crossover.html
+++ b/_modules/pycellga/tests/test_byte_uniform_crossover.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_byte_uniform_crossover — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_byte_uniform_crossover — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_byte_uniform_crossover
    • 
+      
  • pycellga.tests.test_byte_uniform_crossover
    • 
       
  • 
       
    • 
   

@@ -70,7 +70,7 @@
           

            

              
-  
Source code for src.tests.test_byte_uniform_crossover
+  
Source code for pycellga.tests.test_byte_uniform_crossover
 import pytest
 import numpy.random as randomgenerator
 from individual import Individual, GeneType
@@ -79,13 +79,13 @@ 
Source code for src.tests.test_byte_uniform_crossover
import struct
 
 

-[docs]
+[docs]
 class MockProblem(AbstractProblem):
     """
     A mock problem class for testing purposes.
     """
 

-[docs]
+[docs]
     def f(self, x: list) -> float:
         """
         A mock fitness function that simply sums the chromosome values.
@@ -105,7 +105,7 @@ 
Source code for src.tests.test_byte_uniform_crossover

-[docs]
+[docs]
 @pytest.fixture
 def setup_parents():
     """
@@ -126,7 +126,7 @@ 
Source code for src.tests.test_byte_uniform_crossover

-[docs]
+[docs]
 @pytest.fixture
 def setup_problem():
     """
@@ -141,7 +141,7 @@ 
Source code for src.tests.test_byte_uniform_crossover

-[docs]
+[docs]
 def test_byte_uniform_crossover(setup_parents, setup_problem):
     """
     Test the ByteUniformCrossover function implementation.
diff --git a/_modules/src/tests/test_chichinadze_function.html b/_modules/pycellga/tests/test_chichinadze_function.html
similarity index 94%
rename from _modules/src/tests/test_chichinadze_function.html
rename to _modules/pycellga/tests/test_chichinadze_function.html
index 8a66055..1e5ba55 100644
--- a/_modules/src/tests/test_chichinadze_function.html
+++ b/_modules/pycellga/tests/test_chichinadze_function.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_chichinadze_function — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_chichinadze_function — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_chichinadze_function
    • 
+      
  • pycellga.tests.test_chichinadze_function
    • 
       
  • 
       
    • 
   

@@ -70,12 +70,12 @@
           

            

              
-  
Source code for src.tests.test_chichinadze_function
+  
Source code for pycellga.tests.test_chichinadze_function
 import pytest
 from problems.single_objective.continuous.chichinadze import Chichinadze
 
 

-[docs]
+[docs]
 @pytest.fixture
 def setup_chichinadze():
     """
@@ -90,7 +90,7 @@ 
Source code for src.tests.test_chichinadze_function

-[docs]
+[docs]
 def test_chichinadze_function(setup_chichinadze):
     """
     Test the Chichinadze function implementation.
diff --git a/_modules/src/tests/test_compact_13.html b/_modules/pycellga/tests/test_compact_13.html
similarity index 95%
rename from _modules/src/tests/test_compact_13.html
rename to _modules/pycellga/tests/test_compact_13.html
index 1bafe89..50da854 100644
--- a/_modules/src/tests/test_compact_13.html
+++ b/_modules/pycellga/tests/test_compact_13.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_compact_13 — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_compact_13 — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_compact_13
    • 
+      
  • pycellga.tests.test_compact_13
    • 
       
  • 
       
    • 
   

@@ -70,11 +70,11 @@
           

            

              
-  
Source code for src.tests.test_compact_13
+  
Source code for pycellga.tests.test_compact_13
 from neighborhoods.compact_13 import Compact13
 
 

-[docs]
+[docs]
 def test_compact_13():
     """
     Test the Compact13 class for calculating neighbor positions in a grid.
diff --git a/_modules/src/tests/test_compact_21.html b/_modules/pycellga/tests/test_compact_21.html
similarity index 95%
rename from _modules/src/tests/test_compact_21.html
rename to _modules/pycellga/tests/test_compact_21.html
index cc3bc37..21a833b 100644
--- a/_modules/src/tests/test_compact_21.html
+++ b/_modules/pycellga/tests/test_compact_21.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_compact_21 — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_compact_21 — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_compact_21
    • 
+      
  • pycellga.tests.test_compact_21
    • 
       
  • 
       
    • 
   

@@ -70,11 +70,11 @@
           

            

              
-  
Source code for src.tests.test_compact_21
+  
Source code for pycellga.tests.test_compact_21
 from neighborhoods.compact_21 import Compact21
 
 

-[docs]
+[docs]
 def test_compact_21():
     """
     Test the Compact21 class for calculating neighbor positions in a grid.
diff --git a/_modules/src/tests/test_compact_25.html b/_modules/pycellga/tests/test_compact_25.html
similarity index 95%
rename from _modules/src/tests/test_compact_25.html
rename to _modules/pycellga/tests/test_compact_25.html
index e6a8c87..c90e18b 100644
--- a/_modules/src/tests/test_compact_25.html
+++ b/_modules/pycellga/tests/test_compact_25.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_compact_25 — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_compact_25 — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_compact_25
    • 
+      
  • pycellga.tests.test_compact_25
    • 
       
  • 
       
    • 
   

@@ -70,11 +70,11 @@
           

            

              
-  
Source code for src.tests.test_compact_25
+  
Source code for pycellga.tests.test_compact_25
 from neighborhoods.compact_25 import Compact25
 
 

-[docs]
+[docs]
 def test_compact_25():
     """
     Test the Compact25 class for calculating neighbor positions in a grid.
diff --git a/_modules/src/tests/test_compact_9.html b/_modules/pycellga/tests/test_compact_9.html
similarity index 95%
rename from _modules/src/tests/test_compact_9.html
rename to _modules/pycellga/tests/test_compact_9.html
index 988f995..6f41874 100644
--- a/_modules/src/tests/test_compact_9.html
+++ b/_modules/pycellga/tests/test_compact_9.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_compact_9 — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_compact_9 — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_compact_9
    • 
+      
  • pycellga.tests.test_compact_9
    • 
       
  • 
       
    • 
   

@@ -70,11 +70,11 @@
           

            

              
-  
Source code for src.tests.test_compact_9
+  
Source code for pycellga.tests.test_compact_9
 from neighborhoods.compact_9 import Compact9
 
 

-[docs]
+[docs]
 def test_compact_9():
     """
     Test the Compact9 class for calculating neighbor positions in a grid.
diff --git a/_modules/src/tests/test_count_sat.html b/_modules/pycellga/tests/test_count_sat.html
similarity index 94%
rename from _modules/src/tests/test_count_sat.html
rename to _modules/pycellga/tests/test_count_sat.html
index 246c460..14961d9 100644
--- a/_modules/src/tests/test_count_sat.html
+++ b/_modules/pycellga/tests/test_count_sat.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_count_sat — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_count_sat — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_count_sat
    • 
+      
  • pycellga.tests.test_count_sat
    • 
       
  • 
       
    • 
   

@@ -70,11 +70,11 @@
           

            

              
-  
Source code for src.tests.test_count_sat
+  
Source code for pycellga.tests.test_count_sat
 from problems.single_objective.discrete.binary.count_sat import CountSat
 
 

-[docs]
+[docs]
 def test_count_sat():
     """
     Test the CountSat function implementation.
diff --git a/_modules/src/tests/test_dropwave_function.html b/_modules/pycellga/tests/test_dropwave_function.html
similarity index 94%
rename from _modules/src/tests/test_dropwave_function.html
rename to _modules/pycellga/tests/test_dropwave_function.html
index 637bfee..63494f1 100644
--- a/_modules/src/tests/test_dropwave_function.html
+++ b/_modules/pycellga/tests/test_dropwave_function.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_dropwave_function — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_dropwave_function — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_dropwave_function
    • 
+      
  • pycellga.tests.test_dropwave_function
    • 
       
  • 
       
    • 
   

@@ -70,12 +70,12 @@
           

            

              
-  
Source code for src.tests.test_dropwave_function
+  
Source code for pycellga.tests.test_dropwave_function
 import pytest
 from problems.single_objective.continuous.dropwave import Dropwave
 
 

-[docs]
+[docs]
 @pytest.fixture
 def setup_dropwave():
     """
@@ -90,7 +90,7 @@ 
Source code for src.tests.test_dropwave_function

 
 
 

-[docs]
+[docs]
 def test_dropwave_function(setup_dropwave):
     """
     Test the Dropwave function implementation.
diff --git a/_modules/src/tests/test_ecc.html b/_modules/pycellga/tests/test_ecc.html
similarity index 94%
rename from _modules/src/tests/test_ecc.html
rename to _modules/pycellga/tests/test_ecc.html
index 38d2cdb..2917962 100644
--- a/_modules/src/tests/test_ecc.html
+++ b/_modules/pycellga/tests/test_ecc.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_ecc — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_ecc — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_ecc
    • 
+      
  • pycellga.tests.test_ecc
    • 
       
  • 
       
    • 
   

@@ -70,12 +70,12 @@
           

            

              
-  
Source code for src.tests.test_ecc
+  
Source code for pycellga.tests.test_ecc
 import pytest
 from problems.single_objective.discrete.binary.ecc import Ecc
 
 

-[docs]
+[docs]
 @pytest.fixture
 def ecc_instance():
     """
@@ -87,7 +87,7 @@ 
Source code for src.tests.test_ecc
 
 
 

-[docs]
+[docs]
 def test_ecc(ecc_instance):
     """
     Test the ECC function implementation.
diff --git a/_modules/src/tests/test_flat_crossover.html b/_modules/pycellga/tests/test_flat_crossover.html
similarity index 94%
rename from _modules/src/tests/test_flat_crossover.html
rename to _modules/pycellga/tests/test_flat_crossover.html
index a69e28a..69c75c3 100644
--- a/_modules/src/tests/test_flat_crossover.html
+++ b/_modules/pycellga/tests/test_flat_crossover.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_flat_crossover — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_flat_crossover — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_flat_crossover
    • 
+      
  • pycellga.tests.test_flat_crossover
    • 
       
  • 
       
    • 
   

@@ -70,7 +70,7 @@
           

            

              
-  
Source code for src.tests.test_flat_crossover
+  
Source code for pycellga.tests.test_flat_crossover
 import pytest
 import random
 from individual import Individual, GeneType
@@ -78,13 +78,13 @@ 
Source code for src.tests.test_flat_crossover
from recombination.flat_crossover import FlatCrossover  # Replace with the actual path if different
 
 

-[docs]
+[docs]
 class MockProblem(AbstractProblem):
     """
     A mock problem class for testing purposes.
     """
 

-[docs]
+[docs]
     def f(self, x: list) -> float:
         """
         A mock fitness function that simply sums the chromosome values.
@@ -104,7 +104,7 @@ 
Source code for src.tests.test_flat_crossover

-[docs]
+[docs]
 @pytest.fixture
 def setup_parents():
     """
@@ -125,7 +125,7 @@ 
Source code for src.tests.test_flat_crossover

-[docs]
+[docs]
 @pytest.fixture
 def setup_problem():
     """
@@ -140,7 +140,7 @@ 
Source code for src.tests.test_flat_crossover

-[docs]
+[docs]
 def test_flat_crossover(setup_parents, setup_problem):
     """
     Test the FlatCrossover function implementation.
diff --git a/_modules/src/tests/test_float_uniform_mutation.html b/_modules/pycellga/tests/test_float_uniform_mutation.html
similarity index 93%
rename from _modules/src/tests/test_float_uniform_mutation.html
rename to _modules/pycellga/tests/test_float_uniform_mutation.html
index b6e94a7..a4a0c8b 100644
--- a/_modules/src/tests/test_float_uniform_mutation.html
+++ b/_modules/pycellga/tests/test_float_uniform_mutation.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_float_uniform_mutation — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_float_uniform_mutation — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_float_uniform_mutation
    • 
+      
  • pycellga.tests.test_float_uniform_mutation
    • 
       
  • 
       
    • 
   

@@ -70,7 +70,7 @@
           

            

              
-  
Source code for src.tests.test_float_uniform_mutation
+  
Source code for pycellga.tests.test_float_uniform_mutation
 import pytest
 import random
 from individual import Individual, GeneType
@@ -78,13 +78,13 @@ 
Source code for src.tests.test_float_uniform_mutation
from mutation.float_uniform_mutation import FloatUniformMutation  # Replace with the actual path if different
 
 

-[docs]
+[docs]
 class MockProblem(AbstractProblem):
     """
     A mock problem class for testing purposes.
     """
 

-[docs]
+[docs]
     def f(self, x: list) -> float:
         """
         A mock fitness function that simply sums the chromosome values.
@@ -104,7 +104,7 @@ 
Source code for src.tests.test_float_uniform_mutation

-[docs]
+[docs]
 @pytest.fixture
 def setup_individual():
     """
@@ -122,7 +122,7 @@ 
Source code for src.tests.test_float_uniform_mutation

-[docs]
+[docs]
 @pytest.fixture
 def setup_problem():
     """
@@ -137,7 +137,7 @@ 
Source code for src.tests.test_float_uniform_mutation

-[docs]
+[docs]
 def test_float_uniform_mutation(setup_individual, setup_problem):
     """
     Test the FloatUniformMutation function implementation.
diff --git a/_modules/src/tests/test_fms.html b/_modules/pycellga/tests/test_fms.html
similarity index 94%
rename from _modules/src/tests/test_fms.html
rename to _modules/pycellga/tests/test_fms.html
index 7045ec0..f10e3e9 100644
--- a/_modules/src/tests/test_fms.html
+++ b/_modules/pycellga/tests/test_fms.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_fms — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_fms — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_fms
    • 
+      
  • pycellga.tests.test_fms
    • 
       
  • 
       
    • 
   

@@ -70,14 +70,14 @@
           

            

              
-  
Source code for src.tests.test_fms
+  
Source code for pycellga.tests.test_fms
 import pytest
 from problems.abstract_problem import AbstractProblem
 from problems.single_objective.continuous.fms import Fms  # Replace with the actual path if different
 from numpy import random
 
 

-[docs]
+[docs]
 @pytest.fixture
 def fms_instance():
     """
@@ -94,7 +94,7 @@ 
Source code for src.tests.test_fms
 
 
 

-[docs]
+[docs]
 def test_fms(fms_instance):
     """
     Test the Fms function implementation.
diff --git a/_modules/src/tests/test_grid.html b/_modules/pycellga/tests/test_grid.html
similarity index 95%
rename from _modules/src/tests/test_grid.html
rename to _modules/pycellga/tests/test_grid.html
index 13827de..e0dfcb3 100644
--- a/_modules/src/tests/test_grid.html
+++ b/_modules/pycellga/tests/test_grid.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_grid — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_grid — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_grid
    • 
+      
  • pycellga.tests.test_grid
    • 
       
  • 
       
    • 
   

@@ -70,11 +70,11 @@
           

            

              
-  
Source code for src.tests.test_grid
+  
Source code for pycellga.tests.test_grid
 from grid import Grid
 
 

-[docs]
+[docs]
 def test_grid():
     """
     Test the Grid class implementation.
diff --git a/_modules/src/tests/test_griewank_function.html b/_modules/pycellga/tests/test_griewank_function.html
similarity index 94%
rename from _modules/src/tests/test_griewank_function.html
rename to _modules/pycellga/tests/test_griewank_function.html
index dc2adb0..53a7825 100644
--- a/_modules/src/tests/test_griewank_function.html
+++ b/_modules/pycellga/tests/test_griewank_function.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_griewank_function — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_griewank_function — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_griewank_function
    • 
+      
  • pycellga.tests.test_griewank_function
    • 
       
  • 
       
    • 
   

@@ -70,12 +70,12 @@
           

            

              
-  
Source code for src.tests.test_griewank_function
+  
Source code for pycellga.tests.test_griewank_function
 import pytest
 from problems.single_objective.continuous.griewank import Griewank
 
 

-[docs]
+[docs]
 @pytest.fixture
 def setup_griewank():
     """
@@ -90,7 +90,7 @@ 
Source code for src.tests.test_griewank_function

 
 
 

-[docs]
+[docs]
 def test_griewank_function(setup_griewank):
     """
     Test the Griewank function implementation.
diff --git a/_modules/src/tests/test_holzman_function.html b/_modules/pycellga/tests/test_holzman_function.html
similarity index 94%
rename from _modules/src/tests/test_holzman_function.html
rename to _modules/pycellga/tests/test_holzman_function.html
index 4e08c12..8fbbcf9 100644
--- a/_modules/src/tests/test_holzman_function.html
+++ b/_modules/pycellga/tests/test_holzman_function.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_holzman_function — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_holzman_function — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_holzman_function
    • 
+      
  • pycellga.tests.test_holzman_function
    • 
       
  • 
       
    • 
   

@@ -70,12 +70,12 @@
           

            

              
-  
Source code for src.tests.test_holzman_function
+  
Source code for pycellga.tests.test_holzman_function
 import pytest
 from problems.single_objective.continuous.holzman import Holzman
 
 

-[docs]
+[docs]
 @pytest.fixture
 def setup_holzman():
     """
@@ -90,7 +90,7 @@ 
Source code for src.tests.test_holzman_function
<
 
 
 

-[docs]
+[docs]
 def test_holzman_function(setup_holzman):
     """
     Test the Holzman function implementation.
diff --git a/_modules/src/tests/test_individual.html b/_modules/pycellga/tests/test_individual.html
similarity index 91%
rename from _modules/src/tests/test_individual.html
rename to _modules/pycellga/tests/test_individual.html
index 259c4a5..ee00a05 100644
--- a/_modules/src/tests/test_individual.html
+++ b/_modules/pycellga/tests/test_individual.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_individual — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_individual — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_individual
    • 
+      
  • pycellga.tests.test_individual
    • 
       
  • 
       
    • 
   

@@ -70,7 +70,7 @@
           

            

              
-  
Source code for src.tests.test_individual
+  
Source code for pycellga.tests.test_individual
 import pytest
 from numpy import random
 import random as rd
@@ -78,7 +78,7 @@ 
Source code for src.tests.test_individual
 
 
 

-[docs]
+[docs]
 @pytest.fixture
 def setup_individual():
     """
@@ -88,7 +88,7 @@ 
Source code for src.tests.test_individual
 
 
 

-[docs]
+[docs]
 def test_individual_init():
     """
     Test the initialization of the Individual class.
@@ -104,7 +104,7 @@ 
Source code for src.tests.test_individual
 
 
 

-[docs]
+[docs]
 def test_randomize_binary():
     """
     Test the randomization of the chromosome for a binary genome type.
@@ -116,7 +116,7 @@ 
Source code for src.tests.test_individual
 
 
 

-[docs]
+[docs]
 def test_randomize_permutation():
     """
     Test the randomization of the chromosome for a permutation genome type.
@@ -132,7 +132,7 @@ 
Source code for src.tests.test_individual
 
 
 

-[docs]
+[docs]
 def test_randomize_real_valued():
     """
     Test the randomization of the chromosome for a real-valued genome type.
@@ -145,7 +145,7 @@ 
Source code for src.tests.test_individual
 
 
 

-[docs]
+[docs]
 def test_illegal_genome_type():
     """
     Test that an exception is raised when an illegal genome type is provided.
@@ -159,7 +159,7 @@ 
Source code for src.tests.test_individual
 
 
 

-[docs]
+[docs]
 def test_get_set_neighbors_positions():
     """
     Test getting and setting the positions of the individual's neighbors.
@@ -168,7 +168,7 @@ 
Source code for src.tests.test_individual
     
 
 

-[docs]
+[docs]
 def test_get_set_neighbors():
     """
     Test getting and setting the list of neighbors for the individual.
diff --git a/_modules/src/tests/test_insertion_mutation.html b/_modules/pycellga/tests/test_insertion_mutation.html
similarity index 95%
rename from _modules/src/tests/test_insertion_mutation.html
rename to _modules/pycellga/tests/test_insertion_mutation.html
index 4bce711..bfe77c2 100644
--- a/_modules/src/tests/test_insertion_mutation.html
+++ b/_modules/pycellga/tests/test_insertion_mutation.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_insertion_mutation — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_insertion_mutation — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_insertion_mutation
    • 
+      
  • pycellga.tests.test_insertion_mutation
    • 
       
  • 
       
    • 
   

@@ -70,14 +70,14 @@
           

            

              
-  
Source code for src.tests.test_insertion_mutation
+  
Source code for pycellga.tests.test_insertion_mutation
 from mutation.insertion_mutation import InsertionMutation
 from problems.single_objective.discrete.permutation.tsp import Tsp
 from individual import Individual, GeneType
 import random
 
 

-[docs]
+[docs]
 def test_insertion_mutation():
     """
     Test the InsertionMutation class for the Individual class on the TSP problem.
diff --git a/_modules/src/tests/test_levy_function.html b/_modules/pycellga/tests/test_levy_function.html
similarity index 94%
rename from _modules/src/tests/test_levy_function.html
rename to _modules/pycellga/tests/test_levy_function.html
index ce4f733..6c64abb 100644
--- a/_modules/src/tests/test_levy_function.html
+++ b/_modules/pycellga/tests/test_levy_function.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_levy_function — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_levy_function — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_levy_function
    • 
+      
  • pycellga.tests.test_levy_function
    • 
       
  • 
       
    • 
   

@@ -70,12 +70,12 @@
           

            

              
-  
Source code for src.tests.test_levy_function
+  
Source code for pycellga.tests.test_levy_function
 import pytest
 from problems.single_objective.continuous.levy import Levy
 
 

-[docs]
+[docs]
 @pytest.fixture
 def setup_levy():
     """
@@ -85,7 +85,7 @@ 
Source code for src.tests.test_levy_function
-[docs]
+[docs]
 def test_levy_function(setup_levy):
     """
     Test the Levy function implementation.
diff --git a/_modules/src/tests/test_linear_5.html b/_modules/pycellga/tests/test_linear_5.html
similarity index 95%
rename from _modules/src/tests/test_linear_5.html
rename to _modules/pycellga/tests/test_linear_5.html
index 3e9e2aa..d48f18f 100644
--- a/_modules/src/tests/test_linear_5.html
+++ b/_modules/pycellga/tests/test_linear_5.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_linear_5 — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_linear_5 — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_linear_5
    • 
+      
  • pycellga.tests.test_linear_5
    • 
       
  • 
       
    • 
   

@@ -70,11 +70,11 @@
           

            

              
-  
Source code for src.tests.test_linear_5
+  
Source code for pycellga.tests.test_linear_5
 from neighborhoods.linear_5 import Linear5
 
 

-[docs]
+[docs]
 def test_linear_5():
     """
     Test the Linear5 class for calculating neighbor positions in a grid.
diff --git a/_modules/src/tests/test_linear_9.html b/_modules/pycellga/tests/test_linear_9.html
similarity index 95%
rename from _modules/src/tests/test_linear_9.html
rename to _modules/pycellga/tests/test_linear_9.html
index 17a01fe..a6cf136 100644
--- a/_modules/src/tests/test_linear_9.html
+++ b/_modules/pycellga/tests/test_linear_9.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_linear_9 — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_linear_9 — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_linear_9
    • 
+      
  • pycellga.tests.test_linear_9
    • 
       
  • 
       
    • 
   

@@ -70,11 +70,11 @@
           

            

              
-  
Source code for src.tests.test_linear_9
+  
Source code for pycellga.tests.test_linear_9
 from neighborhoods.linear_9 import Linear9
 
 

-[docs]
+[docs]
 def test_linear_9():
     """
     Test the Linear9 class for calculating neighbor positions in a grid.
diff --git a/_modules/src/tests/test_linear_crossover.html b/_modules/pycellga/tests/test_linear_crossover.html
similarity index 94%
rename from _modules/src/tests/test_linear_crossover.html
rename to _modules/pycellga/tests/test_linear_crossover.html
index 1c78a64..4e99afc 100644
--- a/_modules/src/tests/test_linear_crossover.html
+++ b/_modules/pycellga/tests/test_linear_crossover.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_linear_crossover — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_linear_crossover — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_linear_crossover
    • 
+      
  • pycellga.tests.test_linear_crossover
    • 
       
  • 
       
    • 
   

@@ -70,7 +70,7 @@
           

            

              
-  
Source code for src.tests.test_linear_crossover
+  
Source code for pycellga.tests.test_linear_crossover
 import pytest
 import random
 from individual import Individual
@@ -78,13 +78,13 @@ 
Source code for src.tests.test_linear_crossover
<
 from recombination.linear_crossover import LinearCrossover  # Replace with the actual path if different
 
 

-[docs]
+[docs]
 class MockProblem(AbstractProblem):
     """
     A mock problem class for testing purposes.
     """
 

-[docs]
+[docs]
     def f(self, x: list) -> float:
         """
         A mock fitness function that simply sums the chromosome values.
@@ -104,7 +104,7 @@ 
Source code for src.tests.test_linear_crossover
<
 
 
 

-[docs]
+[docs]
 @pytest.fixture
 def setup_parents():
     """
@@ -125,7 +125,7 @@ 
Source code for src.tests.test_linear_crossover
<
 
 
 

-[docs]
+[docs]
 @pytest.fixture
 def setup_problem():
     """
@@ -140,7 +140,7 @@ 
Source code for src.tests.test_linear_crossover
<
 
 
 

-[docs]
+[docs]
 def test_linear_crossover(setup_parents, setup_problem):
     """
     Test the LinearCrossover function implementation.
diff --git a/_modules/src/tests/test_matyas_function.html b/_modules/pycellga/tests/test_matyas_function.html
similarity index 94%
rename from _modules/src/tests/test_matyas_function.html
rename to _modules/pycellga/tests/test_matyas_function.html
index e8f4327..fb4c848 100644
--- a/_modules/src/tests/test_matyas_function.html
+++ b/_modules/pycellga/tests/test_matyas_function.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_matyas_function — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_matyas_function — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_matyas_function
    • 
+      
  • pycellga.tests.test_matyas_function
    • 
       
  • 
       
    • 
   

@@ -70,12 +70,12 @@
           

            

              
-  
Source code for src.tests.test_matyas_function
+  
Source code for pycellga.tests.test_matyas_function
 import pytest
 from problems.single_objective.continuous.matyas import Matyas
 
 

-[docs]
+[docs]
 @pytest.fixture
 def setup_matyas():
     """
@@ -85,7 +85,7 @@ 
Source code for src.tests.test_matyas_function

-[docs]
+[docs]
 def test_matyas_function(setup_matyas):
     """
     Test the Matyas function implementation.
diff --git a/_modules/src/tests/test_maxcut100.html b/_modules/pycellga/tests/test_maxcut100.html
similarity index 94%
rename from _modules/src/tests/test_maxcut100.html
rename to _modules/pycellga/tests/test_maxcut100.html
index ac75eaa..d268d55 100644
--- a/_modules/src/tests/test_maxcut100.html
+++ b/_modules/pycellga/tests/test_maxcut100.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_maxcut100 — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_maxcut100 — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_maxcut100
    • 
+      
  • pycellga.tests.test_maxcut100
    • 
       
  • 
       
    • 
   

@@ -70,14 +70,14 @@
           

            

              
-  
Source code for src.tests.test_maxcut100
+  
Source code for pycellga.tests.test_maxcut100
 import pytest
 
 from problems.single_objective.discrete.binary.maxcut100 import Maxcut100
 
 
 

-[docs]
+[docs]
 @pytest.fixture
 def maxcut_instance():
     """
@@ -89,7 +89,7 @@ 
Source code for src.tests.test_maxcut100
 
 
 

-[docs]
+[docs]
 def test_maxcut100():
     # Maxcut100 sınıfı örneği oluştur
     problem = Maxcut100()
diff --git a/_modules/src/tests/test_maxcut20_01.html b/_modules/pycellga/tests/test_maxcut20_01.html
similarity index 94%
rename from _modules/src/tests/test_maxcut20_01.html
rename to _modules/pycellga/tests/test_maxcut20_01.html
index b297a54..ba417b4 100644
--- a/_modules/src/tests/test_maxcut20_01.html
+++ b/_modules/pycellga/tests/test_maxcut20_01.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_maxcut20_01 — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_maxcut20_01 — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_maxcut20_01
    • 
+      
  • pycellga.tests.test_maxcut20_01
    • 
       
  • 
       
    • 
   

@@ -70,12 +70,12 @@
           

            

              
-  
Source code for src.tests.test_maxcut20_01
+  
Source code for pycellga.tests.test_maxcut20_01
 import pytest
 from problems.single_objective.discrete.binary.maxcut20_01 import Maxcut20_01
 
 

-[docs]
+[docs]
 @pytest.fixture
 def maxcut_instance():
     """
@@ -87,7 +87,7 @@ 
Source code for src.tests.test_maxcut20_01
 
 
 

-[docs]
+[docs]
 def test_maxcut20_01(maxcut_instance):
     """
     Test the MAXCUT function implementation.
diff --git a/_modules/src/tests/test_maxcut20_09.html b/_modules/pycellga/tests/test_maxcut20_09.html
similarity index 94%
rename from _modules/src/tests/test_maxcut20_09.html
rename to _modules/pycellga/tests/test_maxcut20_09.html
index 3b7fbb2..d0c2947 100644
--- a/_modules/src/tests/test_maxcut20_09.html
+++ b/_modules/pycellga/tests/test_maxcut20_09.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_maxcut20_09 — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_maxcut20_09 — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_maxcut20_09
    • 
+      
  • pycellga.tests.test_maxcut20_09
    • 
       
  • 
       
    • 
   

@@ -70,12 +70,12 @@
           

            

              
-  
Source code for src.tests.test_maxcut20_09
+  
Source code for pycellga.tests.test_maxcut20_09
 import pytest
 from problems.single_objective.discrete.binary.maxcut20_09 import Maxcut20_09
 
 

-[docs]
+[docs]
 @pytest.fixture
 def maxcut_instance():
     """
@@ -87,7 +87,7 @@ 
Source code for src.tests.test_maxcut20_09
 
 
 

-[docs]
+[docs]
 def test_maxcut20_09(maxcut_instance):
     """
     Test the MAXCUT function implementation.
diff --git a/_modules/src/tests/test_mmdp.html b/_modules/pycellga/tests/test_mmdp.html
similarity index 95%
rename from _modules/src/tests/test_mmdp.html
rename to _modules/pycellga/tests/test_mmdp.html
index 976ad94..eb3fbd0 100644
--- a/_modules/src/tests/test_mmdp.html
+++ b/_modules/pycellga/tests/test_mmdp.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_mmdp — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_mmdp — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_mmdp
    • 
+      
  • pycellga.tests.test_mmdp
    • 
       
  • 
       
    • 
   

@@ -70,12 +70,12 @@
           

            

              
-  
Source code for src.tests.test_mmdp
+  
Source code for pycellga.tests.test_mmdp
 import pytest
 from problems.single_objective.discrete.binary.mmdp import Mmdp  # Replace with the actual path if different
 
 

-[docs]
+[docs]
 @pytest.fixture
 def mmdp_instance():
     """
@@ -87,7 +87,7 @@ 
Source code for src.tests.test_mmdp
 
 
 

-[docs]
+[docs]
 def test_mmdp_function(mmdp_instance):
     """
     Test the Mmdp function implementation.
diff --git a/_modules/src/tests/test_one_max.html b/_modules/pycellga/tests/test_one_max.html
similarity index 94%
rename from _modules/src/tests/test_one_max.html
rename to _modules/pycellga/tests/test_one_max.html
index 78209c4..e43f57f 100644
--- a/_modules/src/tests/test_one_max.html
+++ b/_modules/pycellga/tests/test_one_max.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_one_max — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_one_max — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_one_max
    • 
+      
  • pycellga.tests.test_one_max
    • 
       
  • 
       
    • 
   

@@ -70,11 +70,11 @@
           

            

              
-  
Source code for src.tests.test_one_max
+  
Source code for pycellga.tests.test_one_max
 from problems.single_objective.discrete.binary.one_max import OneMax
 
 

-[docs]
+[docs]
 def test_one_max():
     """
     Test the OneMax function implementation.
diff --git a/_modules/src/tests/test_one_point_crossover.html b/_modules/pycellga/tests/test_one_point_crossover.html
similarity index 95%
rename from _modules/src/tests/test_one_point_crossover.html
rename to _modules/pycellga/tests/test_one_point_crossover.html
index 97276cb..a58172c 100644
--- a/_modules/src/tests/test_one_point_crossover.html
+++ b/_modules/pycellga/tests/test_one_point_crossover.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_one_point_crossover — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_one_point_crossover — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_one_point_crossover
    • 
+      
  • pycellga.tests.test_one_point_crossover
    • 
       
  • 
       
    • 
   

@@ -70,13 +70,13 @@
           

            

              
-  
Source code for src.tests.test_one_point_crossover
+  
Source code for pycellga.tests.test_one_point_crossover
 from recombination.one_point_crossover import OnePointCrossover
 from individual import Individual, GeneType
 from problems.single_objective.discrete.binary.one_max import OneMax
 
 

-[docs]
+[docs]
 def test_one_point_crossover():
     """
     Test the OnePointCrossover class for generating offspring from two parents.
diff --git a/_modules/src/tests/test_optimizer_alpha_cga.html b/_modules/pycellga/tests/test_optimizer_alpha_cga.html
similarity index 92%
rename from _modules/src/tests/test_optimizer_alpha_cga.html
rename to _modules/pycellga/tests/test_optimizer_alpha_cga.html
index 75df8ba..028d2cf 100644
--- a/_modules/src/tests/test_optimizer_alpha_cga.html
+++ b/_modules/pycellga/tests/test_optimizer_alpha_cga.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_optimizer_alpha_cga — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_optimizer_alpha_cga — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_optimizer_alpha_cga
    • 
+      
  • pycellga.tests.test_optimizer_alpha_cga
    • 
       
  • 
       
    • 
   

@@ -70,14 +70,14 @@
           

            

              
-  
Source code for src.tests.test_optimizer_alpha_cga
+  
Source code for pycellga.tests.test_optimizer_alpha_cga
 import pytest
 from optimizer import alpha_cga, GeneType, TournamentSelection, ByteOnePointCrossover, ByteMutationRandom, OnePointCrossover, BitFlipMutation, PMXCrossover, SwapMutation
 import mpmath as mp
 from typing import List
 
 

-[docs]
+[docs]
 class RealProblem:
     """
     Example problem class to be minimized.
@@ -87,13 +87,13 @@ 
Source code for src.tests.test_optimizer_alpha_cga
    """
     
 

-[docs]
+[docs]
     def __init__(self):
         pass

 
     
 

-[docs]
+[docs]
     def f(self, x):
         """
         Compute the objective function value.
@@ -115,7 +115,7 @@ 
Source code for src.tests.test_optimizer_alpha_cga

-[docs]
+[docs]
 def test_optimizer_alpha_cga_real():
     """Test alpha_cga on a real-valued sum of squares problem."""
     result = alpha_cga(
@@ -139,7 +139,7 @@ 
Source code for src.tests.test_optimizer_alpha_cga

-[docs]
+[docs]
 class BinaryProblem:
     """
     Example problem class to be maximized for binary chromosomes.
@@ -148,13 +148,13 @@ 
Source code for src.tests.test_optimizer_alpha_cga
    """
     
 

-[docs]
+[docs]
     def __init__(self):
         pass

 
     
 

-[docs]
+[docs]
     def f(self, x):
         """
         Compute the objective function value.
@@ -176,7 +176,7 @@ 
Source code for src.tests.test_optimizer_alpha_cga

-[docs]
+[docs]
 def test_optimizer_alpha_cga_binary():
     """Test alpha_cga on a binary OneMax problem."""
     result = alpha_cga(
@@ -201,7 +201,7 @@ 
Source code for src.tests.test_optimizer_alpha_cga

-[docs]
+[docs]
 class PermutationProblem:
     """
     Example problem class to be minimized using a permutation-based approach.
@@ -211,7 +211,7 @@ 
Source code for src.tests.test_optimizer_alpha_cga
    """
     
 

-[docs]
+[docs]
     def __init__(self, target: List[int]):
         """
         Initialize the PermutationProblem with a target permutation.
@@ -225,7 +225,7 @@ 
Source code for src.tests.test_optimizer_alpha_cga

-[docs]
+[docs]
     def f(self, x: List[int]) -> float:
         """
         Compute the objective function value.
@@ -247,7 +247,7 @@ 
Source code for src.tests.test_optimizer_alpha_cga

-[docs]
+[docs]
     def test_optimizer_alpha_cga_permutation(self):
         """
         Test alpha_cga on a permutation-based problem where the target is the identity permutation.
@@ -281,7 +281,7 @@ 
Source code for src.tests.test_optimizer_alpha_cga

-[docs]
+[docs]
 def test_optimizer_alpha_cga_no_variation():
     """Test alpha_cga with no crossover or mutation."""
     result = alpha_cga(
diff --git a/_modules/src/tests/test_optimizer_ccga.html b/_modules/pycellga/tests/test_optimizer_ccga.html
similarity index 91%
rename from _modules/src/tests/test_optimizer_ccga.html
rename to _modules/pycellga/tests/test_optimizer_ccga.html
index b675e3b..34a4e82 100644
--- a/_modules/src/tests/test_optimizer_ccga.html
+++ b/_modules/pycellga/tests/test_optimizer_ccga.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_optimizer_ccga — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_optimizer_ccga — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_optimizer_ccga
    • 
+      
  • pycellga.tests.test_optimizer_ccga
    • 
       
  • 
       
    • 
   

@@ -70,13 +70,13 @@
           

            

              
-  
Source code for src.tests.test_optimizer_ccga
+  
Source code for pycellga.tests.test_optimizer_ccga
 import pytest
 from optimizer import ccga, GeneType, TournamentSelection, ByteOnePointCrossover, ByteMutationRandom, OnePointCrossover, BitFlipMutation, PMXCrossover, SwapMutation
 from typing import List
 
 

-[docs]
+[docs]
 class BinaryProblem:
     """
     Example problem class to be maximized for binary chromosomes.
@@ -85,13 +85,13 @@ 
Source code for src.tests.test_optimizer_ccga
    """
     
 

-[docs]
+[docs]
     def __init__(self):
         pass

 
     
 

-[docs]
+[docs]
     def f(self, x):
         """
         Compute the objective function value.
@@ -113,7 +113,7 @@ 
Source code for src.tests.test_optimizer_ccga

-[docs]
+[docs]
 def test_optimizer_ccga_binary():
     """Test ccga on a binary OneMax problem."""
     result = ccga(
diff --git a/_modules/src/tests/test_optimizer_cga.html b/_modules/pycellga/tests/test_optimizer_cga.html
similarity index 92%
rename from _modules/src/tests/test_optimizer_cga.html
rename to _modules/pycellga/tests/test_optimizer_cga.html
index d2f4f2c..137dd17 100644
--- a/_modules/src/tests/test_optimizer_cga.html
+++ b/_modules/pycellga/tests/test_optimizer_cga.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_optimizer_cga — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_optimizer_cga — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_optimizer_cga
    • 
+      
  • pycellga.tests.test_optimizer_cga
    • 
       
  • 
       
    • 
   

@@ -70,14 +70,14 @@
           

            

              
-  
Source code for src.tests.test_optimizer_cga
+  
Source code for pycellga.tests.test_optimizer_cga
 import pytest
 from optimizer import cga, GeneType, TournamentSelection, ByteOnePointCrossover, ByteMutationRandom, OnePointCrossover, BitFlipMutation, PMXCrossover, SwapMutation
 import mpmath as mp
 from typing import List
 
 

-[docs]
+[docs]
 class RealProblem:
     """
     Example problem class to be minimized.
@@ -87,13 +87,13 @@ 
Source code for src.tests.test_optimizer_cga
    """
     
 

-[docs]
+[docs]
     def __init__(self):
         pass

 
     
 

-[docs]
+[docs]
     def f(self, x):
         """
         Compute the objective function value.
@@ -115,7 +115,7 @@ 
Source code for src.tests.test_optimizer_cga
-[docs]
+[docs]
 def test_optimizer_cga_real():
     """Test CGA on a real-valued sum of squares problem."""
     result = cga(
@@ -138,7 +138,7 @@ 
Source code for src.tests.test_optimizer_cga
-[docs]
+[docs]
 class BinaryProblem:
     """
     Example problem class to be maximized for binary chromosomes.
@@ -147,13 +147,13 @@ 
Source code for src.tests.test_optimizer_cga
    """
     
 

-[docs]
+[docs]
     def __init__(self):
         pass

 
     
 

-[docs]
+[docs]
     def f(self, x):
         """
         Compute the objective function value.
@@ -175,7 +175,7 @@ 
Source code for src.tests.test_optimizer_cga
-[docs]
+[docs]
 def test_optimizer_cga_binary():
     """Test CGA on a binary OneMax problem."""
     result = cga(
@@ -200,7 +200,7 @@ 
Source code for src.tests.test_optimizer_cga
-[docs]
+[docs]
 class PermutationProblem:
     """
     Example problem class to be minimized using a permutation-based approach.
@@ -210,7 +210,7 @@ 
Source code for src.tests.test_optimizer_cga
    """
     
 

-[docs]
+[docs]
     def __init__(self, target: List[int]):
         """
         Initialize the PermutationProblem with a target permutation.
@@ -224,7 +224,7 @@ 
Source code for src.tests.test_optimizer_cga
-[docs]
+[docs]
     def f(self, x: List[int]) -> float:
         """
         Compute the objective function value.
@@ -246,7 +246,7 @@ 
Source code for src.tests.test_optimizer_cga
-[docs]
+[docs]
     def test_optimizer_cga_permutation(self):
         """
         Test CGA on a permutation-based problem where the target is the identity permutation.
@@ -280,7 +280,7 @@ 
Source code for src.tests.test_optimizer_cga
-[docs]
+[docs]
 def test_optimizer_cga_no_variation():
     """Test CGA with no crossover or mutation."""
     result = cga(
diff --git a/_modules/src/tests/test_optimizer_mccga.html b/_modules/pycellga/tests/test_optimizer_mccga.html
similarity index 92%
rename from _modules/src/tests/test_optimizer_mccga.html
rename to _modules/pycellga/tests/test_optimizer_mccga.html
index 4fdd743..f001af7 100644
--- a/_modules/src/tests/test_optimizer_mccga.html
+++ b/_modules/pycellga/tests/test_optimizer_mccga.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_optimizer_mccga — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_optimizer_mccga — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_optimizer_mccga
    • 
+      
  • pycellga.tests.test_optimizer_mccga
    • 
       
  • 
       
    • 
   

@@ -70,14 +70,14 @@
           

            

              
-  
Source code for src.tests.test_optimizer_mccga
+  
Source code for pycellga.tests.test_optimizer_mccga
 import pytest
 from optimizer import mcccga, GeneType, TournamentSelection, ByteOnePointCrossover, ByteMutationRandom, OnePointCrossover, BitFlipMutation, PMXCrossover, SwapMutation
 import mpmath as mp
 from typing import List
 
 

-[docs]
+[docs]
 class RealProblem:
     """
     Example problem class to be minimized.
@@ -87,13 +87,13 @@ 
Source code for src.tests.test_optimizer_mccga
    """
     
 

-[docs]
+[docs]
     def __init__(self):
         pass

 
     
 

-[docs]
+[docs]
     def f(self, x):
         """
         Compute the objective function value.
@@ -115,7 +115,7 @@ 
Source code for src.tests.test_optimizer_mccga

-[docs]
+[docs]
 def test_optimizer_mcccga_binary():
     """Test mcccga on a binary OneMax problem."""
     result = mcccga(
diff --git a/_modules/src/tests/test_optimizer_sync_cga.html b/_modules/pycellga/tests/test_optimizer_sync_cga.html
similarity index 92%
rename from _modules/src/tests/test_optimizer_sync_cga.html
rename to _modules/pycellga/tests/test_optimizer_sync_cga.html
index 76b5220..a16babc 100644
--- a/_modules/src/tests/test_optimizer_sync_cga.html
+++ b/_modules/pycellga/tests/test_optimizer_sync_cga.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_optimizer_sync_cga — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_optimizer_sync_cga — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_optimizer_sync_cga
    • 
+      
  • pycellga.tests.test_optimizer_sync_cga
    • 
       
  • 
       
    • 
   

@@ -70,14 +70,14 @@
           

            

              
-  
Source code for src.tests.test_optimizer_sync_cga
+  
Source code for pycellga.tests.test_optimizer_sync_cga
 import pytest
 from optimizer import sync_cga, GeneType, TournamentSelection, ByteOnePointCrossover, ByteMutationRandom, OnePointCrossover, BitFlipMutation, PMXCrossover, SwapMutation
 import mpmath as mp
 from typing import List
 
 

-[docs]
+[docs]
 class RealProblem:
     """
     Example problem class to be minimized.
@@ -87,13 +87,13 @@ 
Source code for src.tests.test_optimizer_sync_cga
    """
     
 

-[docs]
+[docs]
     def __init__(self):
         pass

 
     
 

-[docs]
+[docs]
     def f(self, x):
         """
         Compute the objective function value.
@@ -115,7 +115,7 @@ 
Source code for src.tests.test_optimizer_sync_cga

-[docs]
+[docs]
 def test_optimizer_sync_cga_real():
     """Test sync_cga on a real-valued sum of squares problem."""
     result = sync_cga(
@@ -138,7 +138,7 @@ 
Source code for src.tests.test_optimizer_sync_cga

-[docs]
+[docs]
 class BinaryProblem:
     """
     Example problem class to be maximized for binary chromosomes.
@@ -147,13 +147,13 @@ 
Source code for src.tests.test_optimizer_sync_cga
    """
     
 

-[docs]
+[docs]
     def __init__(self):
         pass

 
     
 

-[docs]
+[docs]
     def f(self, x):
         """
         Compute the objective function value.
@@ -175,7 +175,7 @@ 
Source code for src.tests.test_optimizer_sync_cga

-[docs]
+[docs]
 def test_optimizer_sync_cga_binary():
     """Test sync_cga on a binary OneMax problem."""
     result = sync_cga(
@@ -200,7 +200,7 @@ 
Source code for src.tests.test_optimizer_sync_cga

-[docs]
+[docs]
 class PermutationProblem:
     """
     Example problem class to be minimized using a permutation-based approach.
@@ -210,7 +210,7 @@ 
Source code for src.tests.test_optimizer_sync_cga
    """
     
 

-[docs]
+[docs]
     def __init__(self, target: List[int]):
         """
         Initialize the PermutationProblem with a target permutation.
@@ -224,7 +224,7 @@ 
Source code for src.tests.test_optimizer_sync_cga

-[docs]
+[docs]
     def f(self, x: List[int]) -> float:
         """
         Compute the objective function value.
@@ -246,7 +246,7 @@ 
Source code for src.tests.test_optimizer_sync_cga

-[docs]
+[docs]
     def test_optimizer_sync_cga_permutation(self):
         """
         Test sync_cga on a permutation-based problem where the target is the identity permutation.
@@ -280,7 +280,7 @@ 
Source code for src.tests.test_optimizer_sync_cga

-[docs]
+[docs]
 def test_optimizer_sync_cga_no_variation():
     """Test sync_cga with no crossover or mutation."""
     result = sync_cga(
diff --git a/_modules/src/tests/test_peak.html b/_modules/pycellga/tests/test_peak.html
similarity index 95%
rename from _modules/src/tests/test_peak.html
rename to _modules/pycellga/tests/test_peak.html
index be17cdc..fee8f58 100644
--- a/_modules/src/tests/test_peak.html
+++ b/_modules/pycellga/tests/test_peak.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_peak — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_peak — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_peak
    • 
+      
  • pycellga.tests.test_peak
    • 
       
  • 
       
    • 
   

@@ -70,13 +70,13 @@
           

            

              
-  
Source code for src.tests.test_peak
+  
Source code for pycellga.tests.test_peak
 import pytest
 from numpy import random
 from problems.single_objective.discrete.binary.peak import Peak  
 
 

-[docs]
+[docs]
 @pytest.fixture
 def peak_instance():
     """
@@ -88,7 +88,7 @@ 
Source code for src.tests.test_peak
 
 
 

-[docs]
+[docs]
 def test_peak(peak_instance):
     """
     Test the Peak function implementation.
diff --git a/_modules/src/tests/test_pmx_crossover.html b/_modules/pycellga/tests/test_pmx_crossover.html
similarity index 95%
rename from _modules/src/tests/test_pmx_crossover.html
rename to _modules/pycellga/tests/test_pmx_crossover.html
index c412ddc..1f65032 100644
--- a/_modules/src/tests/test_pmx_crossover.html
+++ b/_modules/pycellga/tests/test_pmx_crossover.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_pmx_crossover — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_pmx_crossover — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_pmx_crossover
    • 
+      
  • pycellga.tests.test_pmx_crossover
    • 
       
  • 
       
    • 
   

@@ -70,14 +70,14 @@
           

            

              
-  
Source code for src.tests.test_pmx_crossover
+  
Source code for pycellga.tests.test_pmx_crossover
 from problems.single_objective.discrete.permutation.tsp import Tsp
 from recombination.pmx_crossover import PMXCrossover
 from individual import Individual, GeneType
 import random
 
 

-[docs]
+[docs]
 def test_pmx_crossover():
     """
     Test the PMXCrossover class for generating offspring from two permutation parents.
diff --git a/_modules/src/tests/test_population.html b/_modules/pycellga/tests/test_population.html
similarity index 92%
rename from _modules/src/tests/test_population.html
rename to _modules/pycellga/tests/test_population.html
index c064b30..8b1ddfb 100644
--- a/_modules/src/tests/test_population.html
+++ b/_modules/pycellga/tests/test_population.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_population — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_population — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_population
    • 
+      
  • pycellga.tests.test_population
    • 
       
  • 
       
    • 
   

@@ -70,7 +70,7 @@
           

            

              
-  
Source code for src.tests.test_population
+  
Source code for pycellga.tests.test_population
 import pytest
 from individual import Individual, GeneType
 from grid import Grid
@@ -82,7 +82,7 @@ 
Source code for src.tests.test_population
 
 
 

-[docs]
+[docs]
 class MockProblem(AbstractProblem):
     """
     A mock problem for testing purposes.
@@ -94,7 +94,7 @@ 
Source code for src.tests.test_population
     """
 
 

-[docs]
+[docs]
     def f(self, chromosome: List[float]) -> float:
         return sum(chromosome)

 

@@ -102,7 +102,7 @@ 
Source code for src.tests.test_population
 
 
 

-[docs]
+[docs]
 @pytest.fixture
 def setup_population():
     """
@@ -126,7 +126,7 @@ 
Source code for src.tests.test_population
 
 
 

-[docs]
+[docs]
 def test_initial_population_size(setup_population):
     """
     Test the size of the initial population.
@@ -147,7 +147,7 @@ 
Source code for src.tests.test_population
 
 
 

-[docs]
+[docs]
 def test_fitness_evaluation(setup_population):
     """
     Test the fitness evaluation of the individuals.
@@ -171,7 +171,7 @@ 
Source code for src.tests.test_population
 
 
 

-[docs]
+[docs]
 def test_neighborhood_assignment(setup_population):
     """
     Test the neighborhood assignment for the individuals.
diff --git a/_modules/src/tests/test_pow_function.html b/_modules/pycellga/tests/test_pow_function.html
similarity index 94%
rename from _modules/src/tests/test_pow_function.html
rename to _modules/pycellga/tests/test_pow_function.html
index e2df345..f887eb8 100644
--- a/_modules/src/tests/test_pow_function.html
+++ b/_modules/pycellga/tests/test_pow_function.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_pow_function — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_pow_function — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_pow_function
    • 
+      
  • pycellga.tests.test_pow_function
    • 
       
  • 
       
    • 
   

@@ -70,12 +70,12 @@
           

            

              
-  
Source code for src.tests.test_pow_function
+  
Source code for pycellga.tests.test_pow_function
 from problems.abstract_problem import AbstractProblem
 from numpy import power as pw
 
 

-[docs]
+[docs]
 class Pow(AbstractProblem):
     """
     Pow function implementation for optimization problems.
@@ -99,7 +99,7 @@ 
Source code for src.tests.test_pow_function
     """
 
 

-[docs]
+[docs]
     def f(self, x: list) -> float:
         """
         Calculate the Pow function value for a given list of variables.
diff --git a/_modules/src/tests/test_powell_function.html b/_modules/pycellga/tests/test_powell_function.html
similarity index 94%
rename from _modules/src/tests/test_powell_function.html
rename to _modules/pycellga/tests/test_powell_function.html
index 02800d8..1884075 100644
--- a/_modules/src/tests/test_powell_function.html
+++ b/_modules/pycellga/tests/test_powell_function.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_powell_function — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_powell_function — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_powell_function
    • 
+      
  • pycellga.tests.test_powell_function
    • 
       
  • 
       
    • 
   

@@ -70,12 +70,12 @@
           

            

              
-  
Source code for src.tests.test_powell_function
+  
Source code for pycellga.tests.test_powell_function
 import pytest
 from problems.single_objective.continuous.powell import Powell
 
 

-[docs]
+[docs]
 @pytest.fixture
 def setup_powell():
     """
@@ -85,7 +85,7 @@ 
Source code for src.tests.test_powell_function

-[docs]
+[docs]
 def test_powell_function(setup_powell):
     """
     Test the Powell function implementation.
diff --git a/_modules/src/tests/test_rastrigin.html b/_modules/pycellga/tests/test_rastrigin.html
similarity index 94%
rename from _modules/src/tests/test_rastrigin.html
rename to _modules/pycellga/tests/test_rastrigin.html
index d921135..32d7aac 100644
--- a/_modules/src/tests/test_rastrigin.html
+++ b/_modules/pycellga/tests/test_rastrigin.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_rastrigin — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_rastrigin — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_rastrigin
    • 
+      
  • pycellga.tests.test_rastrigin
    • 
       
  • 
       
    • 
   

@@ -70,11 +70,11 @@
           

            

              
-  
Source code for src.tests.test_rastrigin
+  
Source code for pycellga.tests.test_rastrigin
 from problems.single_objective.continuous.rastrigin import Rastrigin
 
 

-[docs]
+[docs]
 def test_rastrigin():
     """
     Test the Rastrigin function implementation.
diff --git a/_modules/src/tests/test_rosenbrock.html b/_modules/pycellga/tests/test_rosenbrock.html
similarity index 94%
rename from _modules/src/tests/test_rosenbrock.html
rename to _modules/pycellga/tests/test_rosenbrock.html
index 5808633..733363a 100644
--- a/_modules/src/tests/test_rosenbrock.html
+++ b/_modules/pycellga/tests/test_rosenbrock.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_rosenbrock — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_rosenbrock — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_rosenbrock
    • 
+      
  • pycellga.tests.test_rosenbrock
    • 
       
  • 
       
    • 
   

@@ -70,11 +70,11 @@
           

            

              
-  
Source code for src.tests.test_rosenbrock
+  
Source code for pycellga.tests.test_rosenbrock
 from problems.single_objective.continuous.rosenbrock import Rosenbrock
 
 

-[docs]
+[docs]
 def test_rosenbrock():
     """
     Test the Rosenbrock function implementation.
diff --git a/_modules/src/tests/test_rothellipsoid_function.html b/_modules/pycellga/tests/test_rothellipsoid_function.html
similarity index 93%
rename from _modules/src/tests/test_rothellipsoid_function.html
rename to _modules/pycellga/tests/test_rothellipsoid_function.html
index 467e3e9..c3eefd2 100644
--- a/_modules/src/tests/test_rothellipsoid_function.html
+++ b/_modules/pycellga/tests/test_rothellipsoid_function.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_rothellipsoid_function — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_rothellipsoid_function — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_rothellipsoid_function
    • 
+      
  • pycellga.tests.test_rothellipsoid_function
    • 
       
  • 
       
    • 
   

@@ -70,12 +70,12 @@
           

            

              
-  
Source code for src.tests.test_rothellipsoid_function
+  
Source code for pycellga.tests.test_rothellipsoid_function
 import pytest
 from problems.single_objective.continuous.rothellipsoid import Rothellipsoid
 
 

-[docs]
+[docs]
 @pytest.fixture
 def setup_rothellipsoid():
     """
@@ -85,7 +85,7 @@ 
Source code for src.tests.test_rothellipsoid_function

-[docs]
+[docs]
 def test_rothellipsoid_function(setup_rothellipsoid):
     """
     Test the Rotated Hyper-Ellipsoid function implementation.
diff --git a/_modules/src/tests/test_roulette_wheel_selection.html b/_modules/pycellga/tests/test_roulette_wheel_selection.html
similarity index 95%
rename from _modules/src/tests/test_roulette_wheel_selection.html
rename to _modules/pycellga/tests/test_roulette_wheel_selection.html
index 2334c09..10804b9 100644
--- a/_modules/src/tests/test_roulette_wheel_selection.html
+++ b/_modules/pycellga/tests/test_roulette_wheel_selection.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_roulette_wheel_selection — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_roulette_wheel_selection — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_roulette_wheel_selection
    • 
+      
  • pycellga.tests.test_roulette_wheel_selection
    • 
       
  • 
       
    • 
   

@@ -70,14 +70,14 @@
           

            

              
-  
Source code for src.tests.test_roulette_wheel_selection
+  
Source code for pycellga.tests.test_roulette_wheel_selection
 from problems.single_objective.discrete.binary.one_max import OneMax
 from selection.roulette_wheel_selection import RouletteWheelSelection
 from population import Population, OptimizationMethod
 from individual import GeneType
 
 

-[docs]
+[docs]
 def test_roulette_wheel_selection():
     """
     Test the RouletteWheelSelection class implementation.
diff --git a/_modules/src/tests/test_schaffer2_function.html b/_modules/pycellga/tests/test_schaffer2_function.html
similarity index 93%
rename from _modules/src/tests/test_schaffer2_function.html
rename to _modules/pycellga/tests/test_schaffer2_function.html
index a6abf5e..8e1c212 100644
--- a/_modules/src/tests/test_schaffer2_function.html
+++ b/_modules/pycellga/tests/test_schaffer2_function.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_schaffer2_function — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_schaffer2_function — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_schaffer2_function
    • 
+      
  • pycellga.tests.test_schaffer2_function
    • 
       
  • 
       
    • 
   

@@ -70,12 +70,12 @@
           

            

              
-  
Source code for src.tests.test_schaffer2_function
+  
Source code for pycellga.tests.test_schaffer2_function
 import pytest
 from problems.single_objective.continuous.schaffer2 import Schaffer2
 
 

-[docs]
+[docs]
 @pytest.fixture
 def setup_schaffer2():
     """
@@ -85,7 +85,7 @@ 
Source code for src.tests.test_schaffer2_function

-[docs]
+[docs]
 def test_schaffer2_function(setup_schaffer2):
     """
     Test the Modified Schaffer function #2 implementation.
diff --git a/_modules/src/tests/test_schaffer_function.html b/_modules/pycellga/tests/test_schaffer_function.html
similarity index 93%
rename from _modules/src/tests/test_schaffer_function.html
rename to _modules/pycellga/tests/test_schaffer_function.html
index 8f648fa..c5c274f 100644
--- a/_modules/src/tests/test_schaffer_function.html
+++ b/_modules/pycellga/tests/test_schaffer_function.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_schaffer_function — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_schaffer_function — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_schaffer_function
    • 
+      
  • pycellga.tests.test_schaffer_function
    • 
       
  • 
       
    • 
   

@@ -70,12 +70,12 @@
           

            

              
-  
Source code for src.tests.test_schaffer_function
+  
Source code for pycellga.tests.test_schaffer_function
 import pytest
 from problems.single_objective.continuous.schaffer import Schaffer
 
 

-[docs]
+[docs]
 @pytest.fixture
 def setup_schaffer():
     """
@@ -85,7 +85,7 @@ 
Source code for src.tests.test_schaffer_function

 
 
 

-[docs]
+[docs]
 def test_schaffer_function(setup_schaffer):
     """
     Test the Modified Schaffer function #1 implementation.
diff --git a/_modules/src/tests/test_schwefel.html b/_modules/pycellga/tests/test_schwefel.html
similarity index 95%
rename from _modules/src/tests/test_schwefel.html
rename to _modules/pycellga/tests/test_schwefel.html
index 36fa77e..7627cfd 100644
--- a/_modules/src/tests/test_schwefel.html
+++ b/_modules/pycellga/tests/test_schwefel.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_schwefel — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_schwefel — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_schwefel
    • 
+      
  • pycellga.tests.test_schwefel
    • 
       
  • 
       
    • 
   

@@ -70,11 +70,11 @@
           

            

              
-  
Source code for src.tests.test_schwefel
+  
Source code for pycellga.tests.test_schwefel
 from problems.single_objective.continuous.schwefel import Schwefel
 
 

-[docs]
+[docs]
 def test_schwefel():
     """
     Test the Schwefel function implementation.
diff --git a/_modules/src/tests/test_shuffle_mutation.html b/_modules/pycellga/tests/test_shuffle_mutation.html
similarity index 95%
rename from _modules/src/tests/test_shuffle_mutation.html
rename to _modules/pycellga/tests/test_shuffle_mutation.html
index 069a889..b7d90e7 100644
--- a/_modules/src/tests/test_shuffle_mutation.html
+++ b/_modules/pycellga/tests/test_shuffle_mutation.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_shuffle_mutation — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_shuffle_mutation — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_shuffle_mutation
    • 
+      
  • pycellga.tests.test_shuffle_mutation
    • 
       
  • 
       
    • 
   

@@ -70,14 +70,14 @@
           

            

              
-  
Source code for src.tests.test_shuffle_mutation
+  
Source code for pycellga.tests.test_shuffle_mutation
 from mutation.shuffle_mutation import ShuffleMutation
 from problems.single_objective.discrete.permutation.tsp import Tsp
 from individual import Individual, GeneType
 import random
 
 

-[docs]
+[docs]
 def test_shuffle_mutation():
     """
     Test the ShuffleMutation class for the Individual class on the TSP problem.
diff --git a/_modules/src/tests/test_sphere.html b/_modules/pycellga/tests/test_sphere.html
similarity index 95%
rename from _modules/src/tests/test_sphere.html
rename to _modules/pycellga/tests/test_sphere.html
index 2db7c51..44d78a2 100644
--- a/_modules/src/tests/test_sphere.html
+++ b/_modules/pycellga/tests/test_sphere.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_sphere — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_sphere — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_sphere
    • 
+      
  • pycellga.tests.test_sphere
    • 
       
  • 
       
    • 
   

@@ -70,11 +70,11 @@
           

            

              
-  
Source code for src.tests.test_sphere
+  
Source code for pycellga.tests.test_sphere
 from problems.single_objective.continuous.sphere import Sphere
 
 

-[docs]
+[docs]
 def test_sphere():
     """
     Test the Sphere function implementation.
diff --git a/_modules/src/tests/test_styblinskitang_function.html b/_modules/pycellga/tests/test_styblinskitang_function.html
similarity index 93%
rename from _modules/src/tests/test_styblinskitang_function.html
rename to _modules/pycellga/tests/test_styblinskitang_function.html
index 3067fb0..8292c18 100644
--- a/_modules/src/tests/test_styblinskitang_function.html
+++ b/_modules/pycellga/tests/test_styblinskitang_function.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_styblinskitang_function — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_styblinskitang_function — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_styblinskitang_function
    • 
+      
  • pycellga.tests.test_styblinskitang_function
    • 
       
  • 
       
    • 
   

@@ -70,12 +70,12 @@
           

            

              
-  
Source code for src.tests.test_styblinskitang_function
+  
Source code for pycellga.tests.test_styblinskitang_function
 import pytest
 from problems.single_objective.continuous.styblinskitang import StyblinskiTang
 
 

-[docs]
+[docs]
 @pytest.fixture
 def setup_styblinski_tang():
     """
@@ -85,7 +85,7 @@ 
Source code for src.tests.test_styblinskitang_function

-[docs]
+[docs]
 def test_styblinskitang_function(setup_styblinski_tang):
     """
     Test the Styblinski-Tang function implementation.
diff --git a/_modules/src/tests/test_sumofdifferentpowers_function.html b/_modules/pycellga/tests/test_sumofdifferentpowers_function.html
similarity index 93%
rename from _modules/src/tests/test_sumofdifferentpowers_function.html
rename to _modules/pycellga/tests/test_sumofdifferentpowers_function.html
index 0ffe1c4..ddf07d3 100644
--- a/_modules/src/tests/test_sumofdifferentpowers_function.html
+++ b/_modules/pycellga/tests/test_sumofdifferentpowers_function.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_sumofdifferentpowers_function — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_sumofdifferentpowers_function — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_sumofdifferentpowers_function
    • 
+      
  • pycellga.tests.test_sumofdifferentpowers_function
    • 
       
  • 
       
    • 
   

@@ -70,12 +70,12 @@
           

            

              
-  
Source code for src.tests.test_sumofdifferentpowers_function
+  
Source code for pycellga.tests.test_sumofdifferentpowers_function
 import pytest
 from problems.single_objective.continuous.sumofdifferentpowers import Sumofdifferentpowers  
 
 

-[docs]
+[docs]
 @pytest.fixture
 def setup_sumofdifferentpowers():
     """
@@ -85,7 +85,7 @@ 
Source code for src.tests.test_sumofdifferentpowers_function

-[docs]
+[docs]
 def test_sumofdifferentpowers_function(setup_sumofdifferentpowers):
     """
     Test the Sum of Different Powers function implementation.
diff --git a/_modules/src/tests/test_swap_mutation.html b/_modules/pycellga/tests/test_swap_mutation.html
similarity index 95%
rename from _modules/src/tests/test_swap_mutation.html
rename to _modules/pycellga/tests/test_swap_mutation.html
index 5007719..17cde37 100644
--- a/_modules/src/tests/test_swap_mutation.html
+++ b/_modules/pycellga/tests/test_swap_mutation.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_swap_mutation — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_swap_mutation — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_swap_mutation
    • 
+      
  • pycellga.tests.test_swap_mutation
    • 
       
  • 
       
    • 
   

@@ -70,14 +70,14 @@
           

            

              
-  
Source code for src.tests.test_swap_mutation
+  
Source code for pycellga.tests.test_swap_mutation
 from mutation.swap_mutation import SwapMutation
 from problems.single_objective.discrete.permutation.tsp import Tsp
 from individual import Individual, GeneType
 import random
 
 

-[docs]
+[docs]
 def test_swap_mutation():
     """
     Test the SwapMutation class for the Individual class on the TSP problem.
diff --git a/_modules/src/tests/test_threehumps_function.html b/_modules/pycellga/tests/test_threehumps_function.html
similarity index 93%
rename from _modules/src/tests/test_threehumps_function.html
rename to _modules/pycellga/tests/test_threehumps_function.html
index cf29382..c8f365f 100644
--- a/_modules/src/tests/test_threehumps_function.html
+++ b/_modules/pycellga/tests/test_threehumps_function.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_threehumps_function — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_threehumps_function — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_threehumps_function
    • 
+      
  • pycellga.tests.test_threehumps_function
    • 
       
  • 
       
    • 
   

@@ -70,12 +70,12 @@
           

            

              
-  
Source code for src.tests.test_threehumps_function
+  
Source code for pycellga.tests.test_threehumps_function
 import pytest
 from problems.single_objective.continuous.threehumps import Threehumps
 
 

-[docs]
+[docs]
 @pytest.fixture
 def setup_threehumps():
     """Fixture to provide the Threehumps problem instance."""
@@ -83,7 +83,7 @@ 
Source code for src.tests.test_threehumps_function

-[docs]
+[docs]
 def test_threehumps_function(setup_threehumps):
     """
     Test the Three Hump Camel function implementation.
diff --git a/_modules/src/tests/test_tournament_selection.html b/_modules/pycellga/tests/test_tournament_selection.html
similarity index 95%
rename from _modules/src/tests/test_tournament_selection.html
rename to _modules/pycellga/tests/test_tournament_selection.html
index c74360d..6c83486 100644
--- a/_modules/src/tests/test_tournament_selection.html
+++ b/_modules/pycellga/tests/test_tournament_selection.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_tournament_selection — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_tournament_selection — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_tournament_selection
    • 
+      
  • pycellga.tests.test_tournament_selection
    • 
       
  • 
       
    • 
   

@@ -70,14 +70,14 @@
           

            

              
-  
Source code for src.tests.test_tournament_selection
+  
Source code for pycellga.tests.test_tournament_selection
 from problems.single_objective.discrete.binary.one_max import OneMax
 from selection.tournament_selection import TournamentSelection
 from population import Population, OptimizationMethod
 from individual import GeneType
 
 

-[docs]
+[docs]
 def test_tournament_selection():
     """
     Test the TournamentSelection class implementation.
diff --git a/_modules/src/tests/test_tsp.html b/_modules/pycellga/tests/test_tsp.html
similarity index 95%
rename from _modules/src/tests/test_tsp.html
rename to _modules/pycellga/tests/test_tsp.html
index 6422dba..2e2945a 100644
--- a/_modules/src/tests/test_tsp.html
+++ b/_modules/pycellga/tests/test_tsp.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_tsp — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_tsp — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_tsp
    • 
+      
  • pycellga.tests.test_tsp
    • 
       
  • 
       
    • 
   

@@ -70,12 +70,12 @@
           

            

              
-  
Source code for src.tests.test_tsp
+  
Source code for pycellga.tests.test_tsp
 from problems.single_objective.discrete.permutation.tsp import Tsp
 import random
 
 

-[docs]
+[docs]
 def test_tsp():
     """
     Test the Tsp function implementation.
diff --git a/_modules/src/tests/test_two_opt_mutation.html b/_modules/pycellga/tests/test_two_opt_mutation.html
similarity index 95%
rename from _modules/src/tests/test_two_opt_mutation.html
rename to _modules/pycellga/tests/test_two_opt_mutation.html
index 0ed636d..3883e68 100644
--- a/_modules/src/tests/test_two_opt_mutation.html
+++ b/_modules/pycellga/tests/test_two_opt_mutation.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_two_opt_mutation — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_two_opt_mutation — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_two_opt_mutation
    • 
+      
  • pycellga.tests.test_two_opt_mutation
    • 
       
  • 
       
    • 
   

@@ -70,14 +70,14 @@
           

            

              
-  
Source code for src.tests.test_two_opt_mutation
+  
Source code for pycellga.tests.test_two_opt_mutation
 from mutation.two_opt_mutation import TwoOptMutation
 from problems.single_objective.discrete.permutation.tsp import Tsp
 from individual import Individual, GeneType
 import random
 
 

-[docs]
+[docs]
 def test_two_opt_mutation():
     """
     Test the TwoOptMutation class for the Individual class on the TSP problem.
diff --git a/_modules/src/tests/test_two_point_crossover.html b/_modules/pycellga/tests/test_two_point_crossover.html
similarity index 95%
rename from _modules/src/tests/test_two_point_crossover.html
rename to _modules/pycellga/tests/test_two_point_crossover.html
index bfb4659..4aa13e6 100644
--- a/_modules/src/tests/test_two_point_crossover.html
+++ b/_modules/pycellga/tests/test_two_point_crossover.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_two_point_crossover — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_two_point_crossover — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_two_point_crossover
    • 
+      
  • pycellga.tests.test_two_point_crossover
    • 
       
  • 
       
    • 
   

@@ -70,14 +70,14 @@
           

            

              
-  
Source code for src.tests.test_two_point_crossover
+  
Source code for pycellga.tests.test_two_point_crossover
 import numpy as np
 from problems.single_objective.discrete.binary.one_max import OneMax
 from recombination.two_point_crossover import TwoPointCrossover
 from individual import Individual, GeneType
 
 

-[docs]
+[docs]
 def test_two_point_crossover():
     """
     Test the TwoPointCrossover class implementation.
diff --git a/_modules/src/tests/test_unfair_average_crossover.html b/_modules/pycellga/tests/test_unfair_average_crossover.html
similarity index 94%
rename from _modules/src/tests/test_unfair_average_crossover.html
rename to _modules/pycellga/tests/test_unfair_average_crossover.html
index 266c066..ac86ac2 100644
--- a/_modules/src/tests/test_unfair_average_crossover.html
+++ b/_modules/pycellga/tests/test_unfair_average_crossover.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_unfair_average_crossover — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_unfair_average_crossover — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_unfair_average_crossover
    • 
+      
  • pycellga.tests.test_unfair_average_crossover
    • 
       
  • 
       
    • 
   

@@ -70,7 +70,7 @@
           

            

              
-  
Source code for src.tests.test_unfair_average_crossover
+  
Source code for pycellga.tests.test_unfair_average_crossover
 import pytest
 import random
 from individual import Individual, GeneType
@@ -78,13 +78,13 @@ 
Source code for src.tests.test_unfair_average_crossover
from recombination.unfair_avarage_crossover import UnfairAvarageCrossover  # Replace with the actual path if different
 
 

-[docs]
+[docs]
 class MockProblem(AbstractProblem):
     """
     A mock problem class for testing purposes.
     """
 

-[docs]
+[docs]
     def f(self, x: list) -> float:
         """
         A mock fitness function that simply sums the chromosome values.
@@ -104,7 +104,7 @@ 
Source code for src.tests.test_unfair_average_crossover

-[docs]
+[docs]
 @pytest.fixture
 def setup_parents():
     """
@@ -125,7 +125,7 @@ 
Source code for src.tests.test_unfair_average_crossover

-[docs]
+[docs]
 @pytest.fixture
 def setup_problem():
     """
@@ -140,7 +140,7 @@ 
Source code for src.tests.test_unfair_average_crossover

-[docs]
+[docs]
 def test_unfair_average_crossover(setup_parents, setup_problem):
     """
     Test the UnfairAvarageCrossover function implementation.
diff --git a/_modules/src/tests/test_uniform_crossover.html b/_modules/pycellga/tests/test_uniform_crossover.html
similarity index 95%
rename from _modules/src/tests/test_uniform_crossover.html
rename to _modules/pycellga/tests/test_uniform_crossover.html
index 642cfa9..4c628a3 100644
--- a/_modules/src/tests/test_uniform_crossover.html
+++ b/_modules/pycellga/tests/test_uniform_crossover.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_uniform_crossover — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_uniform_crossover — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_uniform_crossover
    • 
+      
  • pycellga.tests.test_uniform_crossover
    • 
       
  • 
       
    • 
   

@@ -70,13 +70,13 @@
           

            

              
-  
Source code for src.tests.test_uniform_crossover
+  
Source code for pycellga.tests.test_uniform_crossover
 from problems.single_objective.discrete.binary.one_max import OneMax
 from recombination.uniform_crossover import UniformCrossover
 from individual import Individual, GeneType
 
 

-[docs]
+[docs]
 def test_uniform_crossover():
     """
     Test the UniformCrossover class implementation.
diff --git a/_modules/src/tests/test_zakharov_function.html b/_modules/pycellga/tests/test_zakharov_function.html
similarity index 94%
rename from _modules/src/tests/test_zakharov_function.html
rename to _modules/pycellga/tests/test_zakharov_function.html
index a0d737c..0c01c8c 100644
--- a/_modules/src/tests/test_zakharov_function.html
+++ b/_modules/pycellga/tests/test_zakharov_function.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_zakharov_function — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_zakharov_function — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_zakharov_function
    • 
+      
  • pycellga.tests.test_zakharov_function
    • 
       
  • 
       
    • 
   

@@ -70,13 +70,13 @@
           

            

              
-  
Source code for src.tests.test_zakharov_function
+  
Source code for pycellga.tests.test_zakharov_function
 import pytest
 from problems.single_objective.continuous.zakharov import Zakharov
 
 
 

-[docs]
+[docs]
 def test_zakharov_function():
     """
     Test the Zakharov function implementation.
diff --git a/_modules/src/tests/test_zettle_function.html b/_modules/pycellga/tests/test_zettle_function.html
similarity index 94%
rename from _modules/src/tests/test_zettle_function.html
rename to _modules/pycellga/tests/test_zettle_function.html
index add8254..54679b5 100644
--- a/_modules/src/tests/test_zettle_function.html
+++ b/_modules/pycellga/tests/test_zettle_function.html
@@ -3,7 +3,7 @@
 
   
   
-  src.tests.test_zettle_function — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.tests.test_zettle_function — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -61,7 +61,7 @@
   
    
       
    • 
           
  • Module code
    • 
-      
  • src.tests.test_zettle_function
    • 
+      
  • pycellga.tests.test_zettle_function
    • 
       
  • 
       
    • 
   

@@ -70,12 +70,12 @@
           

            

              
-  
Source code for src.tests.test_zettle_function
+  
Source code for pycellga.tests.test_zettle_function
 import pytest
 from problems.single_objective.continuous.zettle import Zettle
 
 

-[docs]
+[docs]
 @pytest.fixture
 def setup_zettle():
     """
@@ -85,7 +85,7 @@ 
Source code for src.tests.test_zettle_function

-[docs]
+[docs]
 def test_zettle_function(setup_zettle):
     """
     Test the Zettle function implementation.
diff --git a/_sources/modules.rst.txt b/_sources/modules.rst.txt
index 1d567b6..452e130 100644
--- a/_sources/modules.rst.txt
+++ b/_sources/modules.rst.txt
@@ -4,10 +4,4 @@ pycellga
 .. toctree::
    :maxdepth: 4
 
-   src
-   byte_operators
-   grid
-   individual
-   optimizer
-   population
-
+   pycellga
diff --git a/_sources/pycellga.example.rst.txt b/_sources/pycellga.example.rst.txt
new file mode 100644
index 0000000..fb2219f
--- /dev/null
+++ b/_sources/pycellga.example.rst.txt
@@ -0,0 +1,53 @@
+pycellga.example package
+========================
+
+Submodules
+----------
+
+pycellga.example.example\_alpha\_cga module
+-------------------------------------------
+
+.. automodule:: pycellga.example.example_alpha_cga
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.example.example\_ccga module
+-------------------------------------
+
+.. automodule:: pycellga.example.example_ccga
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.example.example\_cga module
+------------------------------------
+
+.. automodule:: pycellga.example.example_cga
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.example.example\_mcccga module
+---------------------------------------
+
+.. automodule:: pycellga.example.example_mcccga
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.example.example\_sync\_cga module
+------------------------------------------
+
+.. automodule:: pycellga.example.example_sync_cga
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Module contents
+---------------
+
+.. automodule:: pycellga.example
+   :members:
+   :undoc-members:
+   :show-inheritance:
diff --git a/_sources/pycellga.mutation.rst.txt b/_sources/pycellga.mutation.rst.txt
new file mode 100644
index 0000000..123848b
--- /dev/null
+++ b/_sources/pycellga.mutation.rst.txt
@@ -0,0 +1,85 @@
+pycellga.mutation package
+=========================
+
+Submodules
+----------
+
+pycellga.mutation.bit\_flip\_mutation module
+--------------------------------------------
+
+.. automodule:: pycellga.mutation.bit_flip_mutation
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.mutation.byte\_mutation module
+---------------------------------------
+
+.. automodule:: pycellga.mutation.byte_mutation
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.mutation.byte\_mutation\_random module
+-----------------------------------------------
+
+.. automodule:: pycellga.mutation.byte_mutation_random
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.mutation.float\_uniform\_mutation module
+-------------------------------------------------
+
+.. automodule:: pycellga.mutation.float_uniform_mutation
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.mutation.insertion\_mutation module
+--------------------------------------------
+
+.. automodule:: pycellga.mutation.insertion_mutation
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.mutation.mutation\_operator module
+-------------------------------------------
+
+.. automodule:: pycellga.mutation.mutation_operator
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.mutation.shuffle\_mutation module
+------------------------------------------
+
+.. automodule:: pycellga.mutation.shuffle_mutation
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.mutation.swap\_mutation module
+---------------------------------------
+
+.. automodule:: pycellga.mutation.swap_mutation
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.mutation.two\_opt\_mutation module
+-------------------------------------------
+
+.. automodule:: pycellga.mutation.two_opt_mutation
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Module contents
+---------------
+
+.. automodule:: pycellga.mutation
+   :members:
+   :undoc-members:
+   :show-inheritance:
diff --git a/_sources/pycellga.neighborhoods.rst.txt b/_sources/pycellga.neighborhoods.rst.txt
new file mode 100644
index 0000000..4df161d
--- /dev/null
+++ b/_sources/pycellga.neighborhoods.rst.txt
@@ -0,0 +1,61 @@
+pycellga.neighborhoods package
+==============================
+
+Submodules
+----------
+
+pycellga.neighborhoods.compact\_13 module
+-----------------------------------------
+
+.. automodule:: pycellga.neighborhoods.compact_13
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.neighborhoods.compact\_21 module
+-----------------------------------------
+
+.. automodule:: pycellga.neighborhoods.compact_21
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.neighborhoods.compact\_25 module
+-----------------------------------------
+
+.. automodule:: pycellga.neighborhoods.compact_25
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.neighborhoods.compact\_9 module
+----------------------------------------
+
+.. automodule:: pycellga.neighborhoods.compact_9
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.neighborhoods.linear\_5 module
+---------------------------------------
+
+.. automodule:: pycellga.neighborhoods.linear_5
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.neighborhoods.linear\_9 module
+---------------------------------------
+
+.. automodule:: pycellga.neighborhoods.linear_9
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Module contents
+---------------
+
+.. automodule:: pycellga.neighborhoods
+   :members:
+   :undoc-members:
+   :show-inheritance:
diff --git a/_sources/pycellga.problems.rst.txt b/_sources/pycellga.problems.rst.txt
new file mode 100644
index 0000000..a59cb89
--- /dev/null
+++ b/_sources/pycellga.problems.rst.txt
@@ -0,0 +1,29 @@
+pycellga.problems package
+=========================
+
+Subpackages
+-----------
+
+.. toctree::
+   :maxdepth: 4
+
+   pycellga.problems.single_objective
+
+Submodules
+----------
+
+pycellga.problems.abstract\_problem module
+------------------------------------------
+
+.. automodule:: pycellga.problems.abstract_problem
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Module contents
+---------------
+
+.. automodule:: pycellga.problems
+   :members:
+   :undoc-members:
+   :show-inheritance:
diff --git a/_sources/pycellga.problems.single_objective.continuous.rst.txt b/_sources/pycellga.problems.single_objective.continuous.rst.txt
new file mode 100644
index 0000000..7c9be72
--- /dev/null
+++ b/_sources/pycellga.problems.single_objective.continuous.rst.txt
@@ -0,0 +1,205 @@
+pycellga.problems.single\_objective.continuous package
+======================================================
+
+Submodules
+----------
+
+pycellga.problems.single\_objective.continuous.ackley module
+------------------------------------------------------------
+
+.. automodule:: pycellga.problems.single_objective.continuous.ackley
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.problems.single\_objective.continuous.bentcigar module
+---------------------------------------------------------------
+
+.. automodule:: pycellga.problems.single_objective.continuous.bentcigar
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.problems.single\_objective.continuous.bohachevsky module
+-----------------------------------------------------------------
+
+.. automodule:: pycellga.problems.single_objective.continuous.bohachevsky
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.problems.single\_objective.continuous.chichinadze module
+-----------------------------------------------------------------
+
+.. automodule:: pycellga.problems.single_objective.continuous.chichinadze
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.problems.single\_objective.continuous.dropwave module
+--------------------------------------------------------------
+
+.. automodule:: pycellga.problems.single_objective.continuous.dropwave
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.problems.single\_objective.continuous.fms module
+---------------------------------------------------------
+
+.. automodule:: pycellga.problems.single_objective.continuous.fms
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.problems.single\_objective.continuous.griewank module
+--------------------------------------------------------------
+
+.. automodule:: pycellga.problems.single_objective.continuous.griewank
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.problems.single\_objective.continuous.holzman module
+-------------------------------------------------------------
+
+.. automodule:: pycellga.problems.single_objective.continuous.holzman
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.problems.single\_objective.continuous.levy module
+----------------------------------------------------------
+
+.. automodule:: pycellga.problems.single_objective.continuous.levy
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.problems.single\_objective.continuous.matyas module
+------------------------------------------------------------
+
+.. automodule:: pycellga.problems.single_objective.continuous.matyas
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.problems.single\_objective.continuous.pow module
+---------------------------------------------------------
+
+.. automodule:: pycellga.problems.single_objective.continuous.pow
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.problems.single\_objective.continuous.powell module
+------------------------------------------------------------
+
+.. automodule:: pycellga.problems.single_objective.continuous.powell
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.problems.single\_objective.continuous.rastrigin module
+---------------------------------------------------------------
+
+.. automodule:: pycellga.problems.single_objective.continuous.rastrigin
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.problems.single\_objective.continuous.rosenbrock module
+----------------------------------------------------------------
+
+.. automodule:: pycellga.problems.single_objective.continuous.rosenbrock
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.problems.single\_objective.continuous.rothellipsoid module
+-------------------------------------------------------------------
+
+.. automodule:: pycellga.problems.single_objective.continuous.rothellipsoid
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.problems.single\_objective.continuous.schaffer module
+--------------------------------------------------------------
+
+.. automodule:: pycellga.problems.single_objective.continuous.schaffer
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.problems.single\_objective.continuous.schaffer2 module
+---------------------------------------------------------------
+
+.. automodule:: pycellga.problems.single_objective.continuous.schaffer2
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.problems.single\_objective.continuous.schwefel module
+--------------------------------------------------------------
+
+.. automodule:: pycellga.problems.single_objective.continuous.schwefel
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.problems.single\_objective.continuous.sphere module
+------------------------------------------------------------
+
+.. automodule:: pycellga.problems.single_objective.continuous.sphere
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.problems.single\_objective.continuous.styblinskitang module
+--------------------------------------------------------------------
+
+.. automodule:: pycellga.problems.single_objective.continuous.styblinskitang
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.problems.single\_objective.continuous.sumofdifferentpowers module
+--------------------------------------------------------------------------
+
+.. automodule:: pycellga.problems.single_objective.continuous.sumofdifferentpowers
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.problems.single\_objective.continuous.threehumps module
+----------------------------------------------------------------
+
+.. automodule:: pycellga.problems.single_objective.continuous.threehumps
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.problems.single\_objective.continuous.zakharov module
+--------------------------------------------------------------
+
+.. automodule:: pycellga.problems.single_objective.continuous.zakharov
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.problems.single\_objective.continuous.zettle module
+------------------------------------------------------------
+
+.. automodule:: pycellga.problems.single_objective.continuous.zettle
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Module contents
+---------------
+
+.. automodule:: pycellga.problems.single_objective.continuous
+   :members:
+   :undoc-members:
+   :show-inheritance:
diff --git a/_sources/pycellga.problems.single_objective.discrete.binary.rst.txt b/_sources/pycellga.problems.single_objective.discrete.binary.rst.txt
new file mode 100644
index 0000000..ea2e50d
--- /dev/null
+++ b/_sources/pycellga.problems.single_objective.discrete.binary.rst.txt
@@ -0,0 +1,85 @@
+pycellga.problems.single\_objective.discrete.binary package
+===========================================================
+
+Submodules
+----------
+
+pycellga.problems.single\_objective.discrete.binary.count\_sat module
+---------------------------------------------------------------------
+
+.. automodule:: pycellga.problems.single_objective.discrete.binary.count_sat
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.problems.single\_objective.discrete.binary.ecc module
+--------------------------------------------------------------
+
+.. automodule:: pycellga.problems.single_objective.discrete.binary.ecc
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.problems.single\_objective.discrete.binary.fms module
+--------------------------------------------------------------
+
+.. automodule:: pycellga.problems.single_objective.discrete.binary.fms
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.problems.single\_objective.discrete.binary.maxcut100 module
+--------------------------------------------------------------------
+
+.. automodule:: pycellga.problems.single_objective.discrete.binary.maxcut100
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.problems.single\_objective.discrete.binary.maxcut20\_01 module
+-----------------------------------------------------------------------
+
+.. automodule:: pycellga.problems.single_objective.discrete.binary.maxcut20_01
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.problems.single\_objective.discrete.binary.maxcut20\_09 module
+-----------------------------------------------------------------------
+
+.. automodule:: pycellga.problems.single_objective.discrete.binary.maxcut20_09
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.problems.single\_objective.discrete.binary.mmdp module
+---------------------------------------------------------------
+
+.. automodule:: pycellga.problems.single_objective.discrete.binary.mmdp
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.problems.single\_objective.discrete.binary.one\_max module
+-------------------------------------------------------------------
+
+.. automodule:: pycellga.problems.single_objective.discrete.binary.one_max
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.problems.single\_objective.discrete.binary.peak module
+---------------------------------------------------------------
+
+.. automodule:: pycellga.problems.single_objective.discrete.binary.peak
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Module contents
+---------------
+
+.. automodule:: pycellga.problems.single_objective.discrete.binary
+   :members:
+   :undoc-members:
+   :show-inheritance:
diff --git a/_sources/pycellga.problems.single_objective.discrete.permutation.rst.txt b/_sources/pycellga.problems.single_objective.discrete.permutation.rst.txt
new file mode 100644
index 0000000..6f63f68
--- /dev/null
+++ b/_sources/pycellga.problems.single_objective.discrete.permutation.rst.txt
@@ -0,0 +1,21 @@
+pycellga.problems.single\_objective.discrete.permutation package
+================================================================
+
+Submodules
+----------
+
+pycellga.problems.single\_objective.discrete.permutation.tsp module
+-------------------------------------------------------------------
+
+.. automodule:: pycellga.problems.single_objective.discrete.permutation.tsp
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Module contents
+---------------
+
+.. automodule:: pycellga.problems.single_objective.discrete.permutation
+   :members:
+   :undoc-members:
+   :show-inheritance:
diff --git a/_sources/pycellga.problems.single_objective.discrete.rst.txt b/_sources/pycellga.problems.single_objective.discrete.rst.txt
new file mode 100644
index 0000000..620fe43
--- /dev/null
+++ b/_sources/pycellga.problems.single_objective.discrete.rst.txt
@@ -0,0 +1,19 @@
+pycellga.problems.single\_objective.discrete package
+====================================================
+
+Subpackages
+-----------
+
+.. toctree::
+   :maxdepth: 4
+
+   pycellga.problems.single_objective.discrete.binary
+   pycellga.problems.single_objective.discrete.permutation
+
+Module contents
+---------------
+
+.. automodule:: pycellga.problems.single_objective.discrete
+   :members:
+   :undoc-members:
+   :show-inheritance:
diff --git a/_sources/pycellga.problems.single_objective.rst.txt b/_sources/pycellga.problems.single_objective.rst.txt
new file mode 100644
index 0000000..4561c68
--- /dev/null
+++ b/_sources/pycellga.problems.single_objective.rst.txt
@@ -0,0 +1,19 @@
+pycellga.problems.single\_objective package
+===========================================
+
+Subpackages
+-----------
+
+.. toctree::
+   :maxdepth: 4
+
+   pycellga.problems.single_objective.continuous
+   pycellga.problems.single_objective.discrete
+
+Module contents
+---------------
+
+.. automodule:: pycellga.problems.single_objective
+   :members:
+   :undoc-members:
+   :show-inheritance:
diff --git a/_sources/pycellga.recombination.rst.txt b/_sources/pycellga.recombination.rst.txt
new file mode 100644
index 0000000..19e4c58
--- /dev/null
+++ b/_sources/pycellga.recombination.rst.txt
@@ -0,0 +1,109 @@
+pycellga.recombination package
+==============================
+
+Submodules
+----------
+
+pycellga.recombination.arithmetic\_crossover module
+---------------------------------------------------
+
+.. automodule:: pycellga.recombination.arithmetic_crossover
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.recombination.blxalpha\_crossover module
+-------------------------------------------------
+
+.. automodule:: pycellga.recombination.blxalpha_crossover
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.recombination.byte\_one\_point\_crossover module
+---------------------------------------------------------
+
+.. automodule:: pycellga.recombination.byte_one_point_crossover
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.recombination.byte\_uniform\_crossover module
+------------------------------------------------------
+
+.. automodule:: pycellga.recombination.byte_uniform_crossover
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.recombination.flat\_crossover module
+---------------------------------------------
+
+.. automodule:: pycellga.recombination.flat_crossover
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.recombination.linear\_crossover module
+-----------------------------------------------
+
+.. automodule:: pycellga.recombination.linear_crossover
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.recombination.one\_point\_crossover module
+---------------------------------------------------
+
+.. automodule:: pycellga.recombination.one_point_crossover
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.recombination.pmx\_crossover module
+--------------------------------------------
+
+.. automodule:: pycellga.recombination.pmx_crossover
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.recombination.recombination\_operator module
+-----------------------------------------------------
+
+.. automodule:: pycellga.recombination.recombination_operator
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.recombination.two\_point\_crossover module
+---------------------------------------------------
+
+.. automodule:: pycellga.recombination.two_point_crossover
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.recombination.unfair\_avarage\_crossover module
+--------------------------------------------------------
+
+.. automodule:: pycellga.recombination.unfair_avarage_crossover
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.recombination.uniform\_crossover module
+------------------------------------------------
+
+.. automodule:: pycellga.recombination.uniform_crossover
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Module contents
+---------------
+
+.. automodule:: pycellga.recombination
+   :members:
+   :undoc-members:
+   :show-inheritance:
diff --git a/_sources/pycellga.rst.txt b/_sources/pycellga.rst.txt
new file mode 100644
index 0000000..dd76889
--- /dev/null
+++ b/_sources/pycellga.rst.txt
@@ -0,0 +1,67 @@
+pycellga package
+================
+
+Subpackages
+-----------
+
+.. toctree::
+   :maxdepth: 4
+
+   pycellga.example
+   pycellga.mutation
+   pycellga.neighborhoods
+   pycellga.problems
+   pycellga.recombination
+   pycellga.selection
+   pycellga.tests
+
+Submodules
+----------
+
+pycellga.byte\_operators module
+-------------------------------
+
+.. automodule:: pycellga.byte_operators
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.grid module
+--------------------
+
+.. automodule:: pycellga.grid
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.individual module
+--------------------------
+
+.. automodule:: pycellga.individual
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.optimizer module
+-------------------------
+
+.. automodule:: pycellga.optimizer
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.population module
+--------------------------
+
+.. automodule:: pycellga.population
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Module contents
+---------------
+
+.. automodule:: pycellga
+   :members:
+   :undoc-members:
+   :show-inheritance:
diff --git a/_sources/pycellga.selection.rst.txt b/_sources/pycellga.selection.rst.txt
new file mode 100644
index 0000000..adab0ab
--- /dev/null
+++ b/_sources/pycellga.selection.rst.txt
@@ -0,0 +1,37 @@
+pycellga.selection package
+==========================
+
+Submodules
+----------
+
+pycellga.selection.roulette\_wheel\_selection module
+----------------------------------------------------
+
+.. automodule:: pycellga.selection.roulette_wheel_selection
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.selection.selection\_operator module
+---------------------------------------------
+
+.. automodule:: pycellga.selection.selection_operator
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.selection.tournament\_selection module
+-----------------------------------------------
+
+.. automodule:: pycellga.selection.tournament_selection
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Module contents
+---------------
+
+.. automodule:: pycellga.selection
+   :members:
+   :undoc-members:
+   :show-inheritance:
diff --git a/_sources/pycellga.tests.rst.txt b/_sources/pycellga.tests.rst.txt
new file mode 100644
index 0000000..4f676b7
--- /dev/null
+++ b/_sources/pycellga.tests.rst.txt
@@ -0,0 +1,573 @@
+pycellga.tests package
+======================
+
+Submodules
+----------
+
+pycellga.tests.conftest module
+------------------------------
+
+.. automodule:: pycellga.tests.conftest
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_ackley module
+----------------------------------
+
+.. automodule:: pycellga.tests.test_ackley
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_arithmetic\_crossover module
+-------------------------------------------------
+
+.. automodule:: pycellga.tests.test_arithmetic_crossover
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_bentcigar\_function module
+-----------------------------------------------
+
+.. automodule:: pycellga.tests.test_bentcigar_function
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_bit\_flip\_mutation module
+-----------------------------------------------
+
+.. automodule:: pycellga.tests.test_bit_flip_mutation
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_blxalpha\_crossover module
+-----------------------------------------------
+
+.. automodule:: pycellga.tests.test_blxalpha_crossover
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_bohachevsky module
+---------------------------------------
+
+.. automodule:: pycellga.tests.test_bohachevsky
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_byte\_mutation module
+------------------------------------------
+
+.. automodule:: pycellga.tests.test_byte_mutation
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_byte\_mutation\_random module
+--------------------------------------------------
+
+.. automodule:: pycellga.tests.test_byte_mutation_random
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_byte\_one\_point\_crossover module
+-------------------------------------------------------
+
+.. automodule:: pycellga.tests.test_byte_one_point_crossover
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_byte\_operators module
+-------------------------------------------
+
+.. automodule:: pycellga.tests.test_byte_operators
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_byte\_uniform\_crossover module
+----------------------------------------------------
+
+.. automodule:: pycellga.tests.test_byte_uniform_crossover
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_chichinadze\_function module
+-------------------------------------------------
+
+.. automodule:: pycellga.tests.test_chichinadze_function
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_compact\_13 module
+---------------------------------------
+
+.. automodule:: pycellga.tests.test_compact_13
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_compact\_21 module
+---------------------------------------
+
+.. automodule:: pycellga.tests.test_compact_21
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_compact\_25 module
+---------------------------------------
+
+.. automodule:: pycellga.tests.test_compact_25
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_compact\_9 module
+--------------------------------------
+
+.. automodule:: pycellga.tests.test_compact_9
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_count\_sat module
+--------------------------------------
+
+.. automodule:: pycellga.tests.test_count_sat
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_dropwave\_function module
+----------------------------------------------
+
+.. automodule:: pycellga.tests.test_dropwave_function
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_ecc module
+-------------------------------
+
+.. automodule:: pycellga.tests.test_ecc
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_flat\_crossover module
+-------------------------------------------
+
+.. automodule:: pycellga.tests.test_flat_crossover
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_float\_uniform\_mutation module
+----------------------------------------------------
+
+.. automodule:: pycellga.tests.test_float_uniform_mutation
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_fms module
+-------------------------------
+
+.. automodule:: pycellga.tests.test_fms
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_grid module
+--------------------------------
+
+.. automodule:: pycellga.tests.test_grid
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_griewank\_function module
+----------------------------------------------
+
+.. automodule:: pycellga.tests.test_griewank_function
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_holzman\_function module
+---------------------------------------------
+
+.. automodule:: pycellga.tests.test_holzman_function
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_individual module
+--------------------------------------
+
+.. automodule:: pycellga.tests.test_individual
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_insertion\_mutation module
+-----------------------------------------------
+
+.. automodule:: pycellga.tests.test_insertion_mutation
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_levy\_function module
+------------------------------------------
+
+.. automodule:: pycellga.tests.test_levy_function
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_linear\_5 module
+-------------------------------------
+
+.. automodule:: pycellga.tests.test_linear_5
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_linear\_9 module
+-------------------------------------
+
+.. automodule:: pycellga.tests.test_linear_9
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_linear\_crossover module
+---------------------------------------------
+
+.. automodule:: pycellga.tests.test_linear_crossover
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_matyas\_function module
+--------------------------------------------
+
+.. automodule:: pycellga.tests.test_matyas_function
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_maxcut100 module
+-------------------------------------
+
+.. automodule:: pycellga.tests.test_maxcut100
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_maxcut20\_01 module
+----------------------------------------
+
+.. automodule:: pycellga.tests.test_maxcut20_01
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_maxcut20\_09 module
+----------------------------------------
+
+.. automodule:: pycellga.tests.test_maxcut20_09
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_mmdp module
+--------------------------------
+
+.. automodule:: pycellga.tests.test_mmdp
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_one\_max module
+------------------------------------
+
+.. automodule:: pycellga.tests.test_one_max
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_one\_point\_crossover module
+-------------------------------------------------
+
+.. automodule:: pycellga.tests.test_one_point_crossover
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_optimizer\_alpha\_cga module
+-------------------------------------------------
+
+.. automodule:: pycellga.tests.test_optimizer_alpha_cga
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_optimizer\_ccga module
+-------------------------------------------
+
+.. automodule:: pycellga.tests.test_optimizer_ccga
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_optimizer\_cga module
+------------------------------------------
+
+.. automodule:: pycellga.tests.test_optimizer_cga
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_optimizer\_mccga module
+--------------------------------------------
+
+.. automodule:: pycellga.tests.test_optimizer_mccga
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_optimizer\_sync\_cga module
+------------------------------------------------
+
+.. automodule:: pycellga.tests.test_optimizer_sync_cga
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_peak module
+--------------------------------
+
+.. automodule:: pycellga.tests.test_peak
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_pmx\_crossover module
+------------------------------------------
+
+.. automodule:: pycellga.tests.test_pmx_crossover
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_population module
+--------------------------------------
+
+.. automodule:: pycellga.tests.test_population
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_pow\_function module
+-----------------------------------------
+
+.. automodule:: pycellga.tests.test_pow_function
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_powell\_function module
+--------------------------------------------
+
+.. automodule:: pycellga.tests.test_powell_function
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_rastrigin module
+-------------------------------------
+
+.. automodule:: pycellga.tests.test_rastrigin
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_rosenbrock module
+--------------------------------------
+
+.. automodule:: pycellga.tests.test_rosenbrock
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_rothellipsoid\_function module
+---------------------------------------------------
+
+.. automodule:: pycellga.tests.test_rothellipsoid_function
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_roulette\_wheel\_selection module
+------------------------------------------------------
+
+.. automodule:: pycellga.tests.test_roulette_wheel_selection
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_schaffer2\_function module
+-----------------------------------------------
+
+.. automodule:: pycellga.tests.test_schaffer2_function
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_schaffer\_function module
+----------------------------------------------
+
+.. automodule:: pycellga.tests.test_schaffer_function
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_schwefel module
+------------------------------------
+
+.. automodule:: pycellga.tests.test_schwefel
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_shuffle\_mutation module
+---------------------------------------------
+
+.. automodule:: pycellga.tests.test_shuffle_mutation
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_sphere module
+----------------------------------
+
+.. automodule:: pycellga.tests.test_sphere
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_styblinskitang\_function module
+----------------------------------------------------
+
+.. automodule:: pycellga.tests.test_styblinskitang_function
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_sumofdifferentpowers\_function module
+----------------------------------------------------------
+
+.. automodule:: pycellga.tests.test_sumofdifferentpowers_function
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_swap\_mutation module
+------------------------------------------
+
+.. automodule:: pycellga.tests.test_swap_mutation
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_threehumps\_function module
+------------------------------------------------
+
+.. automodule:: pycellga.tests.test_threehumps_function
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_tournament\_selection module
+-------------------------------------------------
+
+.. automodule:: pycellga.tests.test_tournament_selection
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_tsp module
+-------------------------------
+
+.. automodule:: pycellga.tests.test_tsp
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_two\_opt\_mutation module
+----------------------------------------------
+
+.. automodule:: pycellga.tests.test_two_opt_mutation
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_two\_point\_crossover module
+-------------------------------------------------
+
+.. automodule:: pycellga.tests.test_two_point_crossover
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_unfair\_average\_crossover module
+------------------------------------------------------
+
+.. automodule:: pycellga.tests.test_unfair_average_crossover
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_uniform\_crossover module
+----------------------------------------------
+
+.. automodule:: pycellga.tests.test_uniform_crossover
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_zakharov\_function module
+----------------------------------------------
+
+.. automodule:: pycellga.tests.test_zakharov_function
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+pycellga.tests.test\_zettle\_function module
+--------------------------------------------
+
+.. automodule:: pycellga.tests.test_zettle_function
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Module contents
+---------------
+
+.. automodule:: pycellga.tests
+   :members:
+   :undoc-members:
+   :show-inheritance:
diff --git a/_sources/setup.rst.txt b/_sources/setup.rst.txt
new file mode 100644
index 0000000..552eb49
--- /dev/null
+++ b/_sources/setup.rst.txt
@@ -0,0 +1,7 @@
+setup module
+============
+
+.. automodule:: setup
+   :members:
+   :undoc-members:
+   :show-inheritance:
diff --git a/_sources/src.example.rst.txt b/_sources/src.example.rst.txt
deleted file mode 100644
index 032f3f4..0000000
--- a/_sources/src.example.rst.txt
+++ /dev/null
@@ -1,53 +0,0 @@
-src.example package
-===================
-
-Submodules
-----------
-
-src.example.example\_alpha\_cga module
---------------------------------------
-
-.. automodule:: src.example.example_alpha_cga
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.example.example\_ccga module
---------------------------------
-
-.. automodule:: src.example.example_ccga
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.example.example\_cga module
--------------------------------
-
-.. automodule:: src.example.example_cga
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.example.example\_mcccga module
-----------------------------------
-
-.. automodule:: src.example.example_mcccga
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.example.example\_sync\_cga module
--------------------------------------
-
-.. automodule:: src.example.example_sync_cga
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-Module contents
----------------
-
-.. automodule:: src.example
-   :members:
-   :undoc-members:
-   :show-inheritance:
diff --git a/_sources/src.mutation.rst.txt b/_sources/src.mutation.rst.txt
deleted file mode 100644
index 952638e..0000000
--- a/_sources/src.mutation.rst.txt
+++ /dev/null
@@ -1,85 +0,0 @@
-src.mutation package
-====================
-
-Submodules
-----------
-
-src.mutation.bit\_flip\_mutation module
----------------------------------------
-
-.. automodule:: src.mutation.bit_flip_mutation
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.mutation.byte\_mutation module
-----------------------------------
-
-.. automodule:: src.mutation.byte_mutation
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.mutation.byte\_mutation\_random module
-------------------------------------------
-
-.. automodule:: src.mutation.byte_mutation_random
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.mutation.float\_uniform\_mutation module
---------------------------------------------
-
-.. automodule:: src.mutation.float_uniform_mutation
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.mutation.insertion\_mutation module
----------------------------------------
-
-.. automodule:: src.mutation.insertion_mutation
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.mutation.mutation\_operator module
---------------------------------------
-
-.. automodule:: src.mutation.mutation_operator
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.mutation.shuffle\_mutation module
--------------------------------------
-
-.. automodule:: src.mutation.shuffle_mutation
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.mutation.swap\_mutation module
-----------------------------------
-
-.. automodule:: src.mutation.swap_mutation
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.mutation.two\_opt\_mutation module
---------------------------------------
-
-.. automodule:: src.mutation.two_opt_mutation
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-Module contents
----------------
-
-.. automodule:: src.mutation
-   :members:
-   :undoc-members:
-   :show-inheritance:
diff --git a/_sources/src.neighborhoods.rst.txt b/_sources/src.neighborhoods.rst.txt
deleted file mode 100644
index 6ab1e8e..0000000
--- a/_sources/src.neighborhoods.rst.txt
+++ /dev/null
@@ -1,61 +0,0 @@
-src.neighborhoods package
-=========================
-
-Submodules
-----------
-
-src.neighborhoods.compact\_13 module
-------------------------------------
-
-.. automodule:: src.neighborhoods.compact_13
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.neighborhoods.compact\_21 module
-------------------------------------
-
-.. automodule:: src.neighborhoods.compact_21
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.neighborhoods.compact\_25 module
-------------------------------------
-
-.. automodule:: src.neighborhoods.compact_25
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.neighborhoods.compact\_9 module
------------------------------------
-
-.. automodule:: src.neighborhoods.compact_9
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.neighborhoods.linear\_5 module
-----------------------------------
-
-.. automodule:: src.neighborhoods.linear_5
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.neighborhoods.linear\_9 module
-----------------------------------
-
-.. automodule:: src.neighborhoods.linear_9
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-Module contents
----------------
-
-.. automodule:: src.neighborhoods
-   :members:
-   :undoc-members:
-   :show-inheritance:
diff --git a/_sources/src.problems.rst.txt b/_sources/src.problems.rst.txt
deleted file mode 100644
index 2e31853..0000000
--- a/_sources/src.problems.rst.txt
+++ /dev/null
@@ -1,29 +0,0 @@
-src.problems package
-====================
-
-Subpackages
------------
-
-.. toctree::
-   :maxdepth: 4
-
-   src.problems.single_objective
-
-Submodules
-----------
-
-src.problems.abstract\_problem module
--------------------------------------
-
-.. automodule:: src.problems.abstract_problem
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-Module contents
----------------
-
-.. automodule:: src.problems
-   :members:
-   :undoc-members:
-   :show-inheritance:
diff --git a/_sources/src.problems.single_objective.continuous.rst.txt b/_sources/src.problems.single_objective.continuous.rst.txt
deleted file mode 100644
index b348372..0000000
--- a/_sources/src.problems.single_objective.continuous.rst.txt
+++ /dev/null
@@ -1,205 +0,0 @@
-src.problems.single\_objective.continuous package
-=================================================
-
-Submodules
-----------
-
-src.problems.single\_objective.continuous.ackley module
--------------------------------------------------------
-
-.. automodule:: src.problems.single_objective.continuous.ackley
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.problems.single\_objective.continuous.bentcigar module
-----------------------------------------------------------
-
-.. automodule:: src.problems.single_objective.continuous.bentcigar
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.problems.single\_objective.continuous.bohachevsky module
-------------------------------------------------------------
-
-.. automodule:: src.problems.single_objective.continuous.bohachevsky
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.problems.single\_objective.continuous.chichinadze module
-------------------------------------------------------------
-
-.. automodule:: src.problems.single_objective.continuous.chichinadze
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.problems.single\_objective.continuous.dropwave module
----------------------------------------------------------
-
-.. automodule:: src.problems.single_objective.continuous.dropwave
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.problems.single\_objective.continuous.fms module
-----------------------------------------------------
-
-.. automodule:: src.problems.single_objective.continuous.fms
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.problems.single\_objective.continuous.griewank module
----------------------------------------------------------
-
-.. automodule:: src.problems.single_objective.continuous.griewank
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.problems.single\_objective.continuous.holzman module
---------------------------------------------------------
-
-.. automodule:: src.problems.single_objective.continuous.holzman
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.problems.single\_objective.continuous.levy module
------------------------------------------------------
-
-.. automodule:: src.problems.single_objective.continuous.levy
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.problems.single\_objective.continuous.matyas module
--------------------------------------------------------
-
-.. automodule:: src.problems.single_objective.continuous.matyas
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.problems.single\_objective.continuous.pow module
-----------------------------------------------------
-
-.. automodule:: src.problems.single_objective.continuous.pow
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.problems.single\_objective.continuous.powell module
--------------------------------------------------------
-
-.. automodule:: src.problems.single_objective.continuous.powell
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.problems.single\_objective.continuous.rastrigin module
-----------------------------------------------------------
-
-.. automodule:: src.problems.single_objective.continuous.rastrigin
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.problems.single\_objective.continuous.rosenbrock module
------------------------------------------------------------
-
-.. automodule:: src.problems.single_objective.continuous.rosenbrock
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.problems.single\_objective.continuous.rothellipsoid module
---------------------------------------------------------------
-
-.. automodule:: src.problems.single_objective.continuous.rothellipsoid
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.problems.single\_objective.continuous.schaffer module
----------------------------------------------------------
-
-.. automodule:: src.problems.single_objective.continuous.schaffer
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.problems.single\_objective.continuous.schaffer2 module
-----------------------------------------------------------
-
-.. automodule:: src.problems.single_objective.continuous.schaffer2
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.problems.single\_objective.continuous.schwefel module
----------------------------------------------------------
-
-.. automodule:: src.problems.single_objective.continuous.schwefel
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.problems.single\_objective.continuous.sphere module
--------------------------------------------------------
-
-.. automodule:: src.problems.single_objective.continuous.sphere
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.problems.single\_objective.continuous.styblinskitang module
----------------------------------------------------------------
-
-.. automodule:: src.problems.single_objective.continuous.styblinskitang
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.problems.single\_objective.continuous.sumofdifferentpowers module
----------------------------------------------------------------------
-
-.. automodule:: src.problems.single_objective.continuous.sumofdifferentpowers
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.problems.single\_objective.continuous.threehumps module
------------------------------------------------------------
-
-.. automodule:: src.problems.single_objective.continuous.threehumps
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.problems.single\_objective.continuous.zakharov module
----------------------------------------------------------
-
-.. automodule:: src.problems.single_objective.continuous.zakharov
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.problems.single\_objective.continuous.zettle module
--------------------------------------------------------
-
-.. automodule:: src.problems.single_objective.continuous.zettle
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-Module contents
----------------
-
-.. automodule:: src.problems.single_objective.continuous
-   :members:
-   :undoc-members:
-   :show-inheritance:
diff --git a/_sources/src.problems.single_objective.discrete.binary.rst.txt b/_sources/src.problems.single_objective.discrete.binary.rst.txt
deleted file mode 100644
index 3e6c6d2..0000000
--- a/_sources/src.problems.single_objective.discrete.binary.rst.txt
+++ /dev/null
@@ -1,85 +0,0 @@
-src.problems.single\_objective.discrete.binary package
-======================================================
-
-Submodules
-----------
-
-src.problems.single\_objective.discrete.binary.count\_sat module
-----------------------------------------------------------------
-
-.. automodule:: src.problems.single_objective.discrete.binary.count_sat
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.problems.single\_objective.discrete.binary.ecc module
----------------------------------------------------------
-
-.. automodule:: src.problems.single_objective.discrete.binary.ecc
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.problems.single\_objective.discrete.binary.fms module
----------------------------------------------------------
-
-.. automodule:: src.problems.single_objective.discrete.binary.fms
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.problems.single\_objective.discrete.binary.maxcut100 module
----------------------------------------------------------------
-
-.. automodule:: src.problems.single_objective.discrete.binary.maxcut100
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.problems.single\_objective.discrete.binary.maxcut20\_01 module
-------------------------------------------------------------------
-
-.. automodule:: src.problems.single_objective.discrete.binary.maxcut20_01
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.problems.single\_objective.discrete.binary.maxcut20\_09 module
-------------------------------------------------------------------
-
-.. automodule:: src.problems.single_objective.discrete.binary.maxcut20_09
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.problems.single\_objective.discrete.binary.mmdp module
-----------------------------------------------------------
-
-.. automodule:: src.problems.single_objective.discrete.binary.mmdp
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.problems.single\_objective.discrete.binary.one\_max module
---------------------------------------------------------------
-
-.. automodule:: src.problems.single_objective.discrete.binary.one_max
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.problems.single\_objective.discrete.binary.peak module
-----------------------------------------------------------
-
-.. automodule:: src.problems.single_objective.discrete.binary.peak
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-Module contents
----------------
-
-.. automodule:: src.problems.single_objective.discrete.binary
-   :members:
-   :undoc-members:
-   :show-inheritance:
diff --git a/_sources/src.problems.single_objective.discrete.permutation.rst.txt b/_sources/src.problems.single_objective.discrete.permutation.rst.txt
deleted file mode 100644
index 83949cf..0000000
--- a/_sources/src.problems.single_objective.discrete.permutation.rst.txt
+++ /dev/null
@@ -1,21 +0,0 @@
-src.problems.single\_objective.discrete.permutation package
-===========================================================
-
-Submodules
-----------
-
-src.problems.single\_objective.discrete.permutation.tsp module
---------------------------------------------------------------
-
-.. automodule:: src.problems.single_objective.discrete.permutation.tsp
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-Module contents
----------------
-
-.. automodule:: src.problems.single_objective.discrete.permutation
-   :members:
-   :undoc-members:
-   :show-inheritance:
diff --git a/_sources/src.problems.single_objective.discrete.rst.txt b/_sources/src.problems.single_objective.discrete.rst.txt
deleted file mode 100644
index b99c408..0000000
--- a/_sources/src.problems.single_objective.discrete.rst.txt
+++ /dev/null
@@ -1,19 +0,0 @@
-src.problems.single\_objective.discrete package
-===============================================
-
-Subpackages
------------
-
-.. toctree::
-   :maxdepth: 4
-
-   src.problems.single_objective.discrete.binary
-   src.problems.single_objective.discrete.permutation
-
-Module contents
----------------
-
-.. automodule:: src.problems.single_objective.discrete
-   :members:
-   :undoc-members:
-   :show-inheritance:
diff --git a/_sources/src.problems.single_objective.rst.txt b/_sources/src.problems.single_objective.rst.txt
deleted file mode 100644
index 15678ee..0000000
--- a/_sources/src.problems.single_objective.rst.txt
+++ /dev/null
@@ -1,19 +0,0 @@
-src.problems.single\_objective package
-======================================
-
-Subpackages
------------
-
-.. toctree::
-   :maxdepth: 4
-
-   src.problems.single_objective.continuous
-   src.problems.single_objective.discrete
-
-Module contents
----------------
-
-.. automodule:: src.problems.single_objective
-   :members:
-   :undoc-members:
-   :show-inheritance:
diff --git a/_sources/src.recombination.rst.txt b/_sources/src.recombination.rst.txt
deleted file mode 100644
index 11795d6..0000000
--- a/_sources/src.recombination.rst.txt
+++ /dev/null
@@ -1,109 +0,0 @@
-src.recombination package
-=========================
-
-Submodules
-----------
-
-src.recombination.arithmetic\_crossover module
-----------------------------------------------
-
-.. automodule:: src.recombination.arithmetic_crossover
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.recombination.blxalpha\_crossover module
---------------------------------------------
-
-.. automodule:: src.recombination.blxalpha_crossover
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.recombination.byte\_one\_point\_crossover module
-----------------------------------------------------
-
-.. automodule:: src.recombination.byte_one_point_crossover
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.recombination.byte\_uniform\_crossover module
--------------------------------------------------
-
-.. automodule:: src.recombination.byte_uniform_crossover
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.recombination.flat\_crossover module
-----------------------------------------
-
-.. automodule:: src.recombination.flat_crossover
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.recombination.linear\_crossover module
-------------------------------------------
-
-.. automodule:: src.recombination.linear_crossover
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.recombination.one\_point\_crossover module
-----------------------------------------------
-
-.. automodule:: src.recombination.one_point_crossover
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.recombination.pmx\_crossover module
----------------------------------------
-
-.. automodule:: src.recombination.pmx_crossover
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.recombination.recombination\_operator module
-------------------------------------------------
-
-.. automodule:: src.recombination.recombination_operator
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.recombination.two\_point\_crossover module
-----------------------------------------------
-
-.. automodule:: src.recombination.two_point_crossover
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.recombination.unfair\_avarage\_crossover module
----------------------------------------------------
-
-.. automodule:: src.recombination.unfair_avarage_crossover
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.recombination.uniform\_crossover module
--------------------------------------------
-
-.. automodule:: src.recombination.uniform_crossover
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-Module contents
----------------
-
-.. automodule:: src.recombination
-   :members:
-   :undoc-members:
-   :show-inheritance:
diff --git a/_sources/src.rst.txt b/_sources/src.rst.txt
deleted file mode 100644
index a5651a4..0000000
--- a/_sources/src.rst.txt
+++ /dev/null
@@ -1,67 +0,0 @@
-src package
-===========
-
-Subpackages
------------
-
-.. toctree::
-   :maxdepth: 4
-
-   src.example
-   src.mutation
-   src.neighborhoods
-   src.problems
-   src.recombination
-   src.selection
-   src.tests
-
-Submodules
-----------
-
-src.byte\_operators module
---------------------------
-
-.. automodule:: src.byte_operators
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.grid module
----------------
-
-.. automodule:: src.grid
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.individual module
----------------------
-
-.. automodule:: src.individual
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.optimizer module
---------------------
-
-.. automodule:: src.optimizer
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.population module
----------------------
-
-.. automodule:: src.population
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-Module contents
----------------
-
-.. automodule:: src
-   :members:
-   :undoc-members:
-   :show-inheritance:
diff --git a/_sources/src.selection.rst.txt b/_sources/src.selection.rst.txt
deleted file mode 100644
index 86c24d5..0000000
--- a/_sources/src.selection.rst.txt
+++ /dev/null
@@ -1,37 +0,0 @@
-src.selection package
-=====================
-
-Submodules
-----------
-
-src.selection.roulette\_wheel\_selection module
------------------------------------------------
-
-.. automodule:: src.selection.roulette_wheel_selection
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.selection.selection\_operator module
-----------------------------------------
-
-.. automodule:: src.selection.selection_operator
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.selection.tournament\_selection module
-------------------------------------------
-
-.. automodule:: src.selection.tournament_selection
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-Module contents
----------------
-
-.. automodule:: src.selection
-   :members:
-   :undoc-members:
-   :show-inheritance:
diff --git a/_sources/src.tests.rst.txt b/_sources/src.tests.rst.txt
deleted file mode 100644
index a6ee6a5..0000000
--- a/_sources/src.tests.rst.txt
+++ /dev/null
@@ -1,573 +0,0 @@
-src.tests package
-=================
-
-Submodules
-----------
-
-src.tests.conftest module
--------------------------
-
-.. automodule:: src.tests.conftest
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.tests.test\_ackley module
------------------------------
-
-.. automodule:: src.tests.test_ackley
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.tests.test\_arithmetic\_crossover module
---------------------------------------------
-
-.. automodule:: src.tests.test_arithmetic_crossover
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.tests.test\_bentcigar\_function module
-------------------------------------------
-
-.. automodule:: src.tests.test_bentcigar_function
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.tests.test\_bit\_flip\_mutation module
-------------------------------------------
-
-.. automodule:: src.tests.test_bit_flip_mutation
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.tests.test\_blxalpha\_crossover module
-------------------------------------------
-
-.. automodule:: src.tests.test_blxalpha_crossover
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.tests.test\_bohachevsky module
-----------------------------------
-
-.. automodule:: src.tests.test_bohachevsky
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.tests.test\_byte\_mutation module
--------------------------------------
-
-.. automodule:: src.tests.test_byte_mutation
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.tests.test\_byte\_mutation\_random module
----------------------------------------------
-
-.. automodule:: src.tests.test_byte_mutation_random
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.tests.test\_byte\_one\_point\_crossover module
---------------------------------------------------
-
-.. automodule:: src.tests.test_byte_one_point_crossover
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.tests.test\_byte\_operators module
---------------------------------------
-
-.. automodule:: src.tests.test_byte_operators
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.tests.test\_byte\_uniform\_crossover module
------------------------------------------------
-
-.. automodule:: src.tests.test_byte_uniform_crossover
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.tests.test\_chichinadze\_function module
---------------------------------------------
-
-.. automodule:: src.tests.test_chichinadze_function
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.tests.test\_compact\_13 module
-----------------------------------
-
-.. automodule:: src.tests.test_compact_13
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.tests.test\_compact\_21 module
-----------------------------------
-
-.. automodule:: src.tests.test_compact_21
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.tests.test\_compact\_25 module
-----------------------------------
-
-.. automodule:: src.tests.test_compact_25
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.tests.test\_compact\_9 module
----------------------------------
-
-.. automodule:: src.tests.test_compact_9
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.tests.test\_count\_sat module
----------------------------------
-
-.. automodule:: src.tests.test_count_sat
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.tests.test\_dropwave\_function module
------------------------------------------
-
-.. automodule:: src.tests.test_dropwave_function
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.tests.test\_ecc module
---------------------------
-
-.. automodule:: src.tests.test_ecc
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.tests.test\_flat\_crossover module
---------------------------------------
-
-.. automodule:: src.tests.test_flat_crossover
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.tests.test\_float\_uniform\_mutation module
------------------------------------------------
-
-.. automodule:: src.tests.test_float_uniform_mutation
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.tests.test\_fms module
---------------------------
-
-.. automodule:: src.tests.test_fms
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-src.tests.test\_grid module
----------------------------
-
-.. automodule:: src.tests.test_grid
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-src.tests.test\_griewank\_function module
------------------------------------------
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-src.tests.test\_holzman\_function module
-----------------------------------------
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-src.tests.test\_individual module
----------------------------------
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-src.tests.test\_insertion\_mutation module
-------------------------------------------
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-src.tests.test\_linear\_5 module
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-src.tests.test\_linear\_crossover module
-----------------------------------------
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----------------------------------------
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-src.tests.test\_maxcut100 module
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-src.tests.test\_maxcut20\_01 module
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------------------------------------
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-src.tests.test\_one\_max module
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-src.tests.test\_one\_point\_crossover module
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-src.tests.test\_optimizer\_alpha\_cga module
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-src.tests.test\_optimizer\_ccga module
---------------------------------------
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-src.tests.test\_optimizer\_cga module
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-.. automodule:: src.tests.test_optimizer_cga
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-src.tests.test\_optimizer\_mccga module
----------------------------------------
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-src.tests.test\_optimizer\_sync\_cga module
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-src.tests.test\_peak module
----------------------------
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-src.tests.test\_pmx\_crossover module
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-src.tests.test\_population module
----------------------------------
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-src.tests.test\_pow\_function module
-------------------------------------
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-src.tests.test\_powell\_function module
----------------------------------------
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-.. automodule:: src.tests.test_powell_function
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-src.tests.test\_rastrigin module
---------------------------------
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-.. automodule:: src.tests.test_rastrigin
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-   :show-inheritance:
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-src.tests.test\_rosenbrock module
----------------------------------
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-.. automodule:: src.tests.test_rosenbrock
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-src.tests.test\_rothellipsoid\_function module
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-.. automodule:: src.tests.test_rothellipsoid_function
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-src.tests.test\_roulette\_wheel\_selection module
--------------------------------------------------
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-.. automodule:: src.tests.test_roulette_wheel_selection
-   :members:
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-src.tests.test\_schaffer2\_function module
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-.. automodule:: src.tests.test_schaffer2_function
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-src.tests.test\_schaffer\_function module
------------------------------------------
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-.. automodule:: src.tests.test_schaffer_function
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-src.tests.test\_schwefel module
--------------------------------
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-.. automodule:: src.tests.test_schwefel
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-   :show-inheritance:
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-src.tests.test\_shuffle\_mutation module
-----------------------------------------
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-.. automodule:: src.tests.test_shuffle_mutation
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-src.tests.test\_sphere module
------------------------------
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-.. automodule:: src.tests.test_sphere
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-   :show-inheritance:
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-src.tests.test\_styblinskitang\_function module
------------------------------------------------
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-.. automodule:: src.tests.test_styblinskitang_function
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-src.tests.test\_sumofdifferentpowers\_function module
------------------------------------------------------
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-.. automodule:: src.tests.test_sumofdifferentpowers_function
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-src.tests.test\_swap\_mutation module
--------------------------------------
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-.. automodule:: src.tests.test_swap_mutation
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-src.tests.test\_threehumps\_function module
--------------------------------------------
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-src.tests.test\_tournament\_selection module
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-src.tests.test\_two\_opt\_mutation module
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-src.tests.test\_unfair\_average\_crossover module
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PYCELLGA DocumentationContents:

 
diff --git a/modules.html b/modules.html
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Contents:

 
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pycellga

 

 

           

 
   

diff --git a/objects.inv b/objects.inv
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zX}$AALj(EP*0XFv}hiJ&Ptqp<}(Is00C$
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z|G1Bz)es{Yu;PpJ+1OQLG%1W86{ac8PBG$v;!xiG5sn^Xxe*O34e5c}CoQAeIR>VE
z%_S-mQ51oYPjG1<<(Vhqv9diVB8T^nr?}l40f#i*29*iXK7Wxv*LJ{tLOvBz5!h||
z1kH~)SJ4G>LgT;}&H=~>9bBO_@vY($)tW4=?CDnxHK44*k2p$78g5usmU4wOe
z7u5*6Y9Nq?N{BB24#o?mx6n$kB-ARC${dWA~VZfXwBp2O-Me9vDgFe~`C&(*mj8PgPk=iy;o
zu-Vp>xoh^{R;^A3UX>%GSRW&|XFPmpk742~KYwNw!{pd@b6=)EPA%JaM3cg!<)}tm
zM#a^U%3_K!+BpD?%cyAGN?B8&eg&-_qd!tte74Y*5pT94*Nk9}WyL7xxh$jPl%5KE
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wCM$a8^E+Q?R*_TChHLW)(si^&Yp3PLjt?4Python Module Index

 
    

-   s
+   p
    

 
    
-     
+     

      
 

-       s

+       p

      

        
        
-       src
+       pycellga
        

      

        
           
-       src.byte_operators
+       pycellga.byte_operators
        

      

        
           
-       src.example
+       pycellga.example
        

      

        
           
-       src.example.example_alpha_cga
+       pycellga.example.example_alpha_cga
        

      

        
           
-       src.example.example_ccga
+       pycellga.example.example_ccga
        

      

        
           
-       src.example.example_cga
+       pycellga.example.example_cga
        

      

        
           
-       src.example.example_mcccga
+       pycellga.example.example_mcccga
        

      

        
           
-       src.example.example_sync_cga
+       pycellga.example.example_sync_cga
        

      

        
           
-       src.grid
+       pycellga.grid
        

      

        
           
-       src.mutation
+       pycellga.mutation
        

      

        
           
-       src.mutation.bit_flip_mutation
+       pycellga.mutation.bit_flip_mutation
        

      

        
           
-       src.mutation.byte_mutation
+       pycellga.mutation.byte_mutation
        

      

        
           
-       src.mutation.byte_mutation_random
+       pycellga.mutation.byte_mutation_random
        

      

        
           
-       src.mutation.float_uniform_mutation
+       pycellga.mutation.float_uniform_mutation
        

      

        
           
-       src.mutation.insertion_mutation
+       pycellga.mutation.insertion_mutation
        

      

        
           
-       src.mutation.mutation_operator
+       pycellga.mutation.mutation_operator
        

      

        
           
-       src.mutation.shuffle_mutation
+       pycellga.mutation.shuffle_mutation
        

      

        
           
-       src.mutation.swap_mutation
+       pycellga.mutation.swap_mutation
        

      

        
           
-       src.mutation.two_opt_mutation
+       pycellga.mutation.two_opt_mutation
        

      

        
           
-       src.neighborhoods
+       pycellga.neighborhoods
        

      

        
           
-       src.neighborhoods.compact_13
+       pycellga.neighborhoods.compact_13
        

      

        
           
-       src.neighborhoods.compact_21
+       pycellga.neighborhoods.compact_21
        

      

        
           
-       src.neighborhoods.compact_25
+       pycellga.neighborhoods.compact_25
        

      

        
           
-       src.neighborhoods.compact_9
+       pycellga.neighborhoods.compact_9
        

      

        
           
-       src.neighborhoods.linear_5
+       pycellga.neighborhoods.linear_5
        

      

        
           
-       src.neighborhoods.linear_9
+       pycellga.neighborhoods.linear_9
        

      

        
           
-       src.problems
+       pycellga.problems
        

      

        
           
-       src.problems.abstract_problem
+       pycellga.problems.abstract_problem
        

      

        
           
-       src.problems.single_objective
+       pycellga.problems.single_objective
        

      

        
           
-       src.problems.single_objective.continuous
+       pycellga.problems.single_objective.continuous
        

      

        
           
-       src.problems.single_objective.continuous.ackley
+       pycellga.problems.single_objective.continuous.ackley
        

      

        
           
-       src.problems.single_objective.continuous.bentcigar
+       pycellga.problems.single_objective.continuous.bentcigar
        

      

        
           
-       src.problems.single_objective.continuous.bohachevsky
+       pycellga.problems.single_objective.continuous.bohachevsky
        

      

        
           
-       src.problems.single_objective.continuous.chichinadze
+       pycellga.problems.single_objective.continuous.chichinadze
        

      

        
           
-       src.problems.single_objective.continuous.dropwave
+       pycellga.problems.single_objective.continuous.dropwave
        

      

        
           
-       src.problems.single_objective.continuous.fms
+       pycellga.problems.single_objective.continuous.fms
        

      

        
           
-       src.problems.single_objective.continuous.griewank
+       pycellga.problems.single_objective.continuous.griewank
        

      

        
           
-       src.problems.single_objective.continuous.holzman
+       pycellga.problems.single_objective.continuous.holzman
        

      

        
           
-       src.problems.single_objective.continuous.levy
+       pycellga.problems.single_objective.continuous.levy
        

      

        
           
-       src.problems.single_objective.continuous.matyas
+       pycellga.problems.single_objective.continuous.matyas
        

      

        
           
-       src.problems.single_objective.continuous.pow
+       pycellga.problems.single_objective.continuous.pow
        

      

        
           
-       src.problems.single_objective.continuous.powell
+       pycellga.problems.single_objective.continuous.powell
        

      

        
           
-       src.problems.single_objective.continuous.rastrigin
+       pycellga.problems.single_objective.continuous.rastrigin
        

      

        
           
-       src.problems.single_objective.continuous.rosenbrock
+       pycellga.problems.single_objective.continuous.rosenbrock
        

      

        
           
-       src.problems.single_objective.continuous.rothellipsoid
+       pycellga.problems.single_objective.continuous.rothellipsoid
        

      

        
           
-       src.problems.single_objective.continuous.schaffer
+       pycellga.problems.single_objective.continuous.schaffer
        

      

        
           
-       src.problems.single_objective.continuous.schaffer2
+       pycellga.problems.single_objective.continuous.schaffer2
        

      

        
           
-       src.problems.single_objective.continuous.schwefel
+       pycellga.problems.single_objective.continuous.schwefel
        

      

        
           
-       src.problems.single_objective.continuous.sphere
+       pycellga.problems.single_objective.continuous.sphere
        

      

        
           
-       src.problems.single_objective.continuous.styblinskitang
+       pycellga.problems.single_objective.continuous.styblinskitang
        

      

        
           
-       src.problems.single_objective.continuous.sumofdifferentpowers
+       pycellga.problems.single_objective.continuous.sumofdifferentpowers
        

      

        
           
-       src.problems.single_objective.continuous.threehumps
+       pycellga.problems.single_objective.continuous.threehumps
        

      

        
           
-       src.problems.single_objective.continuous.zakharov
+       pycellga.problems.single_objective.continuous.zakharov
        

      

        
           
-       src.problems.single_objective.continuous.zettle
+       pycellga.problems.single_objective.continuous.zettle
        

      

        
           
-       src.problems.single_objective.discrete
+       pycellga.problems.single_objective.discrete
        

      

        
           
-       src.problems.single_objective.discrete.binary
+       pycellga.problems.single_objective.discrete.binary
        

      

        
           
-       src.problems.single_objective.discrete.binary.count_sat
+       pycellga.problems.single_objective.discrete.binary.count_sat
        

      

        
           
-       src.problems.single_objective.discrete.binary.ecc
+       pycellga.problems.single_objective.discrete.binary.ecc
        

      

        
           
-       src.problems.single_objective.discrete.binary.fms
+       pycellga.problems.single_objective.discrete.binary.fms
        

      

        
           
-       src.problems.single_objective.discrete.binary.maxcut100
+       pycellga.problems.single_objective.discrete.binary.maxcut100
        

      

        
           
-       src.problems.single_objective.discrete.binary.maxcut20_01
+       pycellga.problems.single_objective.discrete.binary.maxcut20_01
        

      

        
           
-       src.problems.single_objective.discrete.binary.maxcut20_09
+       pycellga.problems.single_objective.discrete.binary.maxcut20_09
        

      

        
           
-       src.problems.single_objective.discrete.binary.mmdp
+       pycellga.problems.single_objective.discrete.binary.mmdp
        

      

        
           
-       src.problems.single_objective.discrete.binary.one_max
+       pycellga.problems.single_objective.discrete.binary.one_max
        

      

        
           
-       src.problems.single_objective.discrete.binary.peak
+       pycellga.problems.single_objective.discrete.binary.peak
        

      

        
           
-       src.problems.single_objective.discrete.permutation
+       pycellga.problems.single_objective.discrete.permutation
        

      

        
           
-       src.problems.single_objective.discrete.permutation.tsp
+       pycellga.problems.single_objective.discrete.permutation.tsp
        

      

        
           
-       src.recombination
+       pycellga.recombination
        

      

        
           
-       src.recombination.arithmetic_crossover
+       pycellga.recombination.arithmetic_crossover
        

      

        
           
-       src.recombination.blxalpha_crossover
+       pycellga.recombination.blxalpha_crossover
        

      

        
           
-       src.recombination.byte_one_point_crossover
+       pycellga.recombination.byte_one_point_crossover
        

      

        
           
-       src.recombination.byte_uniform_crossover
+       pycellga.recombination.byte_uniform_crossover
        

      

        
           
-       src.recombination.flat_crossover
+       pycellga.recombination.flat_crossover
        

      

        
           
-       src.recombination.linear_crossover
+       pycellga.recombination.linear_crossover
        

      

        
           
-       src.recombination.one_point_crossover
+       pycellga.recombination.one_point_crossover
        

      

        
           
-       src.recombination.pmx_crossover
+       pycellga.recombination.pmx_crossover
        

      

        
           
-       src.recombination.recombination_operator
+       pycellga.recombination.recombination_operator
        

      

        
           
-       src.recombination.two_point_crossover
+       pycellga.recombination.two_point_crossover
        

      

        
           
-       src.recombination.unfair_avarage_crossover
+       pycellga.recombination.unfair_avarage_crossover
        

      

        
           
-       src.recombination.uniform_crossover
+       pycellga.recombination.uniform_crossover
        

      

        
           
-       src.selection
+       pycellga.selection
        

      

        
           
-       src.selection.roulette_wheel_selection
+       pycellga.selection.roulette_wheel_selection
        

      

        
           
-       src.selection.selection_operator
+       pycellga.selection.selection_operator
        

      

        
           
-       src.selection.tournament_selection
+       pycellga.selection.tournament_selection
        

      

        
           
-       src.tests
+       pycellga.tests
        

      

        
           
-       src.tests.conftest
+       pycellga.tests.conftest
        

      

        
           
-       src.tests.test_ackley
+       pycellga.tests.test_ackley
        

      

        
           
-       src.tests.test_arithmetic_crossover
+       pycellga.tests.test_arithmetic_crossover
        

      

        
           
-       src.tests.test_bentcigar_function
+       pycellga.tests.test_bentcigar_function
        

      

        
           
-       src.tests.test_bit_flip_mutation
+       pycellga.tests.test_bit_flip_mutation
        

      

        
           
-       src.tests.test_blxalpha_crossover
+       pycellga.tests.test_blxalpha_crossover
        

      

        
           
-       src.tests.test_bohachevsky
+       pycellga.tests.test_bohachevsky
        

      

        
           
-       src.tests.test_byte_mutation
+       pycellga.tests.test_byte_mutation
        

      

        
           
-       src.tests.test_byte_mutation_random
+       pycellga.tests.test_byte_mutation_random
        

      

        
           
-       src.tests.test_byte_one_point_crossover
+       pycellga.tests.test_byte_one_point_crossover
        

      

        
           
-       src.tests.test_byte_operators
+       pycellga.tests.test_byte_operators
        

      

        
           
-       src.tests.test_byte_uniform_crossover
+       pycellga.tests.test_byte_uniform_crossover
        

      

        
           
-       src.tests.test_chichinadze_function
+       pycellga.tests.test_chichinadze_function
        

      

        
           
-       src.tests.test_compact_13
+       pycellga.tests.test_compact_13
        

      

        
           
-       src.tests.test_compact_21
+       pycellga.tests.test_compact_21
        

      

        
           
-       src.tests.test_compact_25
+       pycellga.tests.test_compact_25
        

      

        
           
-       src.tests.test_compact_9
+       pycellga.tests.test_compact_9
        

      

        
           
-       src.tests.test_count_sat
+       pycellga.tests.test_count_sat
        

      

        
           
-       src.tests.test_dropwave_function
+       pycellga.tests.test_dropwave_function
        

      

        
           
-       src.tests.test_ecc
+       pycellga.tests.test_ecc
        

      

        
           
-       src.tests.test_flat_crossover
+       pycellga.tests.test_flat_crossover
        

      

        
           
-       src.tests.test_float_uniform_mutation
+       pycellga.tests.test_float_uniform_mutation
        

      

        
           
-       src.tests.test_fms
+       pycellga.tests.test_fms
        

      

        
           
-       src.tests.test_grid
+       pycellga.tests.test_grid
        

      

        
           
-       src.tests.test_griewank_function
+       pycellga.tests.test_griewank_function
        

      

        
           
-       src.tests.test_holzman_function
+       pycellga.tests.test_holzman_function
        

      

        
           
-       src.tests.test_individual
+       pycellga.tests.test_individual
        

      

        
           
-       src.tests.test_insertion_mutation
+       pycellga.tests.test_insertion_mutation
        

      

        
           
-       src.tests.test_levy_function
+       pycellga.tests.test_levy_function
        

      

        
           
-       src.tests.test_linear_5
+       pycellga.tests.test_linear_5
        

      

        
           
-       src.tests.test_linear_9
+       pycellga.tests.test_linear_9
        

      

        
           
-       src.tests.test_linear_crossover
+       pycellga.tests.test_linear_crossover
        

      

        
           
-       src.tests.test_matyas_function
+       pycellga.tests.test_matyas_function
        

      

        
           
-       src.tests.test_maxcut100
+       pycellga.tests.test_maxcut100
        

      

        
           
-       src.tests.test_maxcut20_01
+       pycellga.tests.test_maxcut20_01
        

      

        
           
-       src.tests.test_maxcut20_09
+       pycellga.tests.test_maxcut20_09
        

      

        
           
-       src.tests.test_mmdp
+       pycellga.tests.test_mmdp
        

      

        
           
-       src.tests.test_one_max
+       pycellga.tests.test_one_max
        

      

        
           
-       src.tests.test_one_point_crossover
+       pycellga.tests.test_one_point_crossover
        

      

        
           
-       src.tests.test_optimizer_alpha_cga
+       pycellga.tests.test_optimizer_alpha_cga
        

      

        
           
-       src.tests.test_optimizer_ccga
+       pycellga.tests.test_optimizer_ccga
        

      

        
           
-       src.tests.test_optimizer_cga
+       pycellga.tests.test_optimizer_cga
        

      

        
           
-       src.tests.test_optimizer_mccga
+       pycellga.tests.test_optimizer_mccga
        

      

        
           
-       src.tests.test_optimizer_sync_cga
+       pycellga.tests.test_optimizer_sync_cga
        

      

        
           
-       src.tests.test_peak
+       pycellga.tests.test_peak
        

      

        
           
-       src.tests.test_pmx_crossover
+       pycellga.tests.test_pmx_crossover
        

      

        
           
-       src.tests.test_population
+       pycellga.tests.test_population
        

      

        
           
-       src.tests.test_pow_function
+       pycellga.tests.test_pow_function
        

      

        
           
-       src.tests.test_powell_function
+       pycellga.tests.test_powell_function
        

      

        
           
-       src.tests.test_rastrigin
+       pycellga.tests.test_rastrigin
        

      

        
           
-       src.tests.test_rosenbrock
+       pycellga.tests.test_rosenbrock
        

      

        
           
-       src.tests.test_rothellipsoid_function
+       pycellga.tests.test_rothellipsoid_function
        

      

        
           
-       src.tests.test_roulette_wheel_selection
+       pycellga.tests.test_roulette_wheel_selection
        

      

        
           
-       src.tests.test_schaffer2_function
+       pycellga.tests.test_schaffer2_function
        

      

        
           
-       src.tests.test_schaffer_function
+       pycellga.tests.test_schaffer_function
        

      

        
           
-       src.tests.test_schwefel
+       pycellga.tests.test_schwefel
        

      

        
           
-       src.tests.test_shuffle_mutation
+       pycellga.tests.test_shuffle_mutation
        

      

        
           
-       src.tests.test_sphere
+       pycellga.tests.test_sphere
        

      

        
           
-       src.tests.test_styblinskitang_function
+       pycellga.tests.test_styblinskitang_function
        

      

        
           
-       src.tests.test_sumofdifferentpowers_function
+       pycellga.tests.test_sumofdifferentpowers_function
        

      

        
           
-       src.tests.test_swap_mutation
+       pycellga.tests.test_swap_mutation
        

      

        
           
-       src.tests.test_threehumps_function
+       pycellga.tests.test_threehumps_function
        

      

        
           
-       src.tests.test_tournament_selection
+       pycellga.tests.test_tournament_selection
        

      

        
           
-       src.tests.test_tsp
+       pycellga.tests.test_tsp
        

      

        
           
-       src.tests.test_two_opt_mutation
+       pycellga.tests.test_two_opt_mutation
        

      

        
           
-       src.tests.test_two_point_crossover
+       pycellga.tests.test_two_point_crossover
        

      

        
           
-       src.tests.test_unfair_average_crossover
+       pycellga.tests.test_unfair_average_crossover
        

      

        
           
-       src.tests.test_uniform_crossover
+       pycellga.tests.test_uniform_crossover
        

      

        
           
-       src.tests.test_zakharov_function
+       pycellga.tests.test_zakharov_function
        

      

        
           
-       src.tests.test_zettle_function
+       pycellga.tests.test_zettle_function
        

    

 
diff --git a/src.example.html b/pycellga.example.html
similarity index 55%
rename from src.example.html
rename to pycellga.example.html
index 10d5c62..198382a 100644
--- a/src.example.html
+++ b/pycellga.example.html
@@ -4,7 +4,7 @@
   
 
   
-  src.example package — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.example package — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -21,8 +21,8 @@
     
     
     
-    
-     
+    
+     
 
 
  
@@ -47,24 +47,24 @@
               
Contents:

 
    
 
  • pycellga
      
-
    • src package
        
-
      • Subpackages
@@ -86,10 +86,10 @@
   
   
        
@@ -97,28 +97,28 @@
           
        
            
        
              
-  
        
-
        src.example package
        
+  
        
+
        pycellga.example package
        
 
        
 
        Submodules
        
 
        
-
        
-
        src.example.example_alpha_cga module
        
+
        
+
        pycellga.example.example_alpha_cga module
        
 
        
-
        
-class src.example.example_alpha_cga.ExampleProblem[source]
        
+
        
+class pycellga.example.example_alpha_cga.ExampleProblem[source]
        
 
        Bases: object
        
 
        Example problem class to be minimized.
        
 
        This class implements a simple sum of squares function with a global minimum value of 0,
 achieved when all elements of the chromosome are equal to 0.
        
 
        
-
        
-__init__()[source]
        
+
        
+__init__()[source]
        
 
        
 
 
        
-
        
-f(x)[source]
        
+
        
+f(x)[source]
        
 
        Compute the objective function value.
        
 
        This method implements the sum of squares function.
        
 
        
@@ -137,8 +137,8 @@ 
        Submodules
-
        
-src.example.example_alpha_cga.run_alpha_cga_example()[source]
        
+
        
+pycellga.example.example_alpha_cga.run_alpha_cga_example()[source]
        
 
        Run the Alpha Cellular Genetic Algorithm (alpha_cga) using the optimizer module.
        
 
        The alpha_cga is configured with a 5x5 grid, 100 generations, and a chromosome size of 10.
 The problem being solved is an instance of the ExampleProblem class,
@@ -154,22 +154,22 @@ 
        Submodules
-
        src.example.example_ccga module
        
+
        
+
        pycellga.example.example_ccga module
        
 
        
-
        
-class src.example.example_ccga.ExampleProblem[source]
        
+
        
+class pycellga.example.example_ccga.ExampleProblem[source]
        
 
        Bases: object
        
 
        Example problem class to be minimized.
        
 
        This class implements a simple binary optimization problem, where the goal is to maximize the number of 1s.
        
 
        
-
        
-__init__()[source]
        
+
        
+__init__()[source]
        
 
        
 
 
        
-
        
-f(x)[source]
        
+
        
+f(x)[source]
        
 
        Compute the objective function value.
        
 
        This method implements a simple sum of binary values.
        
 
        
@@ -188,8 +188,8 @@ 
        Submodules
-
        
-src.example.example_ccga.run_ccga_example()[source]
        
+
        
+pycellga.example.example_ccga.run_ccga_example()[source]
        
 
        Run the Compact Cellular Genetic Algorithm (ccga) using the optimizer module.
        
 
        The ccga is configured with a 5x5 grid, 100 generations, and a chromosome size of 10.
 The problem being solved is an instance of the ExampleProblem class,
@@ -205,23 +205,23 @@ 
        Submodules
-
        src.example.example_cga module
        
+
        
+
        pycellga.example.example_cga module
        
 
        
-
        
-class src.example.example_cga.ExampleProblem[source]
        
+
        
+class pycellga.example.example_cga.ExampleProblem[source]
        
 
        Bases: object
        
 
        Example problem class to be minimized.
        
 
        This class implements a simple sum of squares function with a global minimum value of 0,
 achieved when all elements of the chromosome are equal to 0.
        
 
        
-
        
-__init__()[source]
        
+
        
+__init__()[source]
        
 
        
 
 
        
-
        
-f(x)[source]
        
+
        
+f(x)[source]
        
 
        Compute the objective function value.
        
 
        This method implements the sum of squares function.
        
 
        
@@ -240,8 +240,8 @@ 
        Submodules
-
        
-src.example.example_cga.run_cga_example()[source]
        
+
        
+pycellga.example.example_cga.run_cga_example()[source]
        
 
        Run the Cellular Genetic Algorithm (cga) using the optimizer module.
        
 
        The cga is configured with a 5x5 grid, 100 generations, and a chromosome size of 5.
 The problem being solved is an instance of the ExampleProblem class,
@@ -257,23 +257,23 @@ 
        Submodules
-
        src.example.example_mcccga module
        
+
        
+
        pycellga.example.example_mcccga module
        
 
        
-
        
-class src.example.example_mcccga.RealProblem[source]
        
+
        
+class pycellga.example.example_mcccga.RealProblem[source]
        
 
        Bases: object
        
 
        Example problem class to be minimized.
        
 
        This class implements a simple sum of squares function with a global minimum value of 0,
 achieved when all elements of the chromosome are equal to 0.
        
 
        
-
        
-__init__()[source]
        
+
        
+__init__()[source]
        
 
        
 
 
        
-
        
-f(x)[source]
        
+
        
+f(x)[source]
        
 
        Compute the objective function value.
        
 
        This method implements the sum of squares function.
        
 
        
@@ -292,8 +292,8 @@ 
        Submodules
-
        
-src.example.example_mcccga.run_mcccga_example()[source]
        
+
        
+pycellga.example.example_mcccga.run_mcccga_example()[source]
        
 
        Run the Machine-Coded Compact Cellular Genetic Algorithm (mcccga)
 using the optimizer module.
        
 
        The mcccga is configured with a 5x5 grid, 100 generations, and a chromosome size of 10.
@@ -310,23 +310,23 @@ 
        Submodules
-
        src.example.example_sync_cga module
        
+
        
+
        pycellga.example.example_sync_cga module
        
 
        
-
        
-class src.example.example_sync_cga.ExampleProblem[source]
        
+
        
+class pycellga.example.example_sync_cga.ExampleProblem[source]
        
 
        Bases: object
        
 
        Example problem class to be minimized.
        
 
        This class implements a simple sum of squares function with a global minimum value of 0,
 achieved when all elements of the chromosome are equal to 0.
        
 
        
-
        
-__init__()[source]
        
+
        
+__init__()[source]
        
 
        
 
 
        
-
        
-f(x)[source]
        
+
        
+f(x)[source]
        
 
        Compute the objective function value.
        
 
        This method implements the sum of squares function.
        
 
        
@@ -345,8 +345,8 @@ 
        Submodules
-
        
-src.example.example_sync_cga.run_sync_cga_example()[source]
        
+
        
+pycellga.example.example_sync_cga.run_sync_cga_example()[source]
        
 
        Run the Synchronous Cellular Genetic Algorithm (sync_cga) using the optimizer module.
        
 
        The sync_cga is configured with a 5x5 grid, 100 generations, and a chromosome size of 5.
 The problem being solved is an instance of the ExampleProblem class,
@@ -362,8 +362,8 @@ 
        Submodules
-
        Module contents
        
+
        
+
        Module contents
        
 
        
 
        
 
@@ -371,8 +371,8 @@ 
        Submodules
-        
-        
+        
+        
     
        
 
   
        
diff --git a/pycellga.html b/pycellga.html
new file mode 100644
index 0000000..1f7d3d6
--- /dev/null
+++ b/pycellga.html
@@ -0,0 +1,1081 @@
+
+
+
+  
+
+  
+  pycellga package — PYCELLGA Documentation 1.0.0 documentation
+      
+      
+
+  
+  
+  
+        
+        
+        
+        
+        
+    
+    
+    
+    
+     
+
+
+ 
+  
        
+    
+
+    
        
+
+      
        
+        
        
+          
        
+  
+  
        
+
        
+          
        
+           
        
+             
+  
        
+
        pycellga package
        
+
        
+
        Subpackages
        
+
        
+
+
        
+
        
+
        
+
        Submodules
        
+
        
+
        
+
        pycellga.byte_operators module
        
+
        
+
        
+pycellga.byte_operators.bits_to_float(bit_list: list[int]) → float[source]
        
+
        Convert a bit representation to its float value.
        
+
        
+
        Parameters:
        
+
        bit_list (list of int) – A list of 32 integers (0 or 1) representing the bit pattern of the float.
        
+
        
+
        Returns:
        
+
        The float value represented by the bit pattern.
        
+
        
+
        Return type:
        
+
        float
        
+
        
+
        
+
        
+
+
        
+
        
+pycellga.byte_operators.bits_to_floats(bit_list: list[int]) → list[float][source]
        
+
        Convert a combined bit representation back to a list of floats.
        
+
        
+
        Parameters:
        
+
        bit_list (list of int) – A list of integers (0 or 1) representing the combined bit patterns of the floats.
        
+
        
+
        Returns:
        
+
        The list of float values represented by the bit pattern.
        
+
        
+
        Return type:
        
+
        list of float
        
+
        
+
        
+
        
+
+
        
+
        
+pycellga.byte_operators.float_to_bits(float_number: float) → list[int][source]
        
+
        Convert a float to its bit representation.
        
+
        
+
        Parameters:
        
+
        float_number (float) – The float number to be converted.
        
+
        
+
        Returns:
        
+
        A list of 32 integers (0 or 1) representing the bit pattern of the float.
        
+
        
+
        Return type:
        
+
        list of int
        
+
        
+
        
+
        
+
+
        
+
        
+pycellga.byte_operators.floats_to_bits(float_list: list[float]) → list[int][source]
        
+
        Convert a list of floats to their combined bit representation.
        
+
        
+
        Parameters:
        
+
        float_list (list of float) – The list of float numbers to be converted.
        
+
        
+
        Returns:
        
+
        A list of integers (0 or 1) representing the combined bit patterns of the floats.
        
+
        
+
        Return type:
        
+
        list of int
        
+
        
+
        
+
        
+
+
        
+
        
+
        pycellga.grid module
        
+
        
+
        
+class pycellga.grid.Grid(n_rows: int, n_cols: int)[source]
        
+
        Bases: object
        
+
        A class to represent a 2D grid.
        
+
        
+
        
+n_rows
        
+
        Number of rows in the grid.
        
+
        
+
        Type:
        
+
        int
        
+
        
+
        
+
        
+
+
        
+
        
+n_cols
        
+
        Number of columns in the grid.
        
+
        
+
        Type:
        
+
        int
        
+
        
+
        
+
        
+
+
        
+
        
+__init__(n_rows: int, n_cols: int)[source]
        
+
        Initialize the Grid with the number of rows and columns.
        
+
        
+
        Parameters:
        
+
          
+
        • n_rows (int) – Number of rows in the grid.
          • 
+
        • n_cols (int) – Number of columns in the grid.
          • 
+
        
+
        
+
        
+
        
+
+
        
+
        
+make_2d_grid() → list[source]
        
+
        Create a 2D grid where each cell is represented by a tuple (row, column).
        
+
        
+
        Returns:
        
+
        A list of tuples where each tuple represents a grid cell.
+Each tuple is of the form (row, column), with rows and columns starting from 1.
        
+
        
+
        Return type:
        
+
        list
        
+
        
+
        
+
        
+
+
        
+
+
        
+
        
+
        pycellga.individual module
        
+
        
+
        
+
        pycellga.optimizer module
        
+
        
+
        
+
        pycellga.population module
        
+
        
+
        
+
        Module contents
        
+
        
+
        
+
+
+           
        
+          
        
+          
+        
        
+      
        
+    
        
+  
        
+   
+
+
+
\ No newline at end of file
diff --git a/pycellga.mutation.html b/pycellga.mutation.html
new file mode 100644
index 0000000..303f607
--- /dev/null
+++ b/pycellga.mutation.html
@@ -0,0 +1,537 @@
+
+
+
+  
+
+  
+  pycellga.mutation package — PYCELLGA Documentation 1.0.0 documentation
+      
+      
+
+  
+  
+  
+        
+        
+        
+        
+        
+    
+    
+    
+    
+     
+
+
+ 
+  
        
+    
+
+    
        
+
+      
        
+        
        
+          
        
+  
+  
        
+
        
+          
        
+           
        
+             
+  
        
+
        pycellga.mutation package
        
+
        
+
        Submodules
        
+
        
+
        
+
        pycellga.mutation.bit_flip_mutation module
        
+
        
+
        
+class pycellga.mutation.bit_flip_mutation.BitFlipMutation(mutation_cand: Individual = None, problem: AbstractProblem = None)[source]
        
+
        Bases: MutationOperator
        
+
        BitFlipMutation performs a bit flip mutation on an individual in a Genetic Algorithm.
        
+
        
+
        Parameters:
        
+
          
+
        • mutation_cand (Individual, optional) – The candidate individual to be mutated (default is None).
          • 
+
        • problem (AbstractProblem, optional) – The problem instance that provides the fitness function (default is None).
          • 
+
        
+
        
+
        
+
        
+
        
+__init__(mutation_cand: Individual = None, problem: AbstractProblem = None)[source]
        
+
        Initialize the BitFlipMutation object.
        
+
        
+
        Parameters:
        
+
          
+
        • mutation_cand (Individual, optional) – The candidate individual to be mutated (default is None).
          • 
+
        • problem (AbstractProblem, optional) – The problem instance that provides the fitness function (default is None).
          • 
+
        
+
        
+
        
+
        
+
+
        
+
        
+mutate() → Individual[source]
        
+
        Perform a bit flip mutation on the candidate individual.
        
+
        A single bit in the candidate’s chromosome is randomly selected and flipped
+(i.e., a 0 is changed to a 1, or a 1 is changed to a 0).
        
+
        
+
        Returns:
        
+
        A new individual with the mutated chromosome.
        
+
        
+
        Return type:
        
+
        Individual
        
+
        
+
        
+
        
+
+
        
+
+
        
+
        
+
        pycellga.mutation.byte_mutation module
        
+
        
+
        
+class pycellga.mutation.byte_mutation.ByteMutation(mutation_cand: Individual = None, problem: AbstractProblem = None)[source]
        
+
        Bases: MutationOperator
        
+
        ByteMutation operator defined in (Satman, 2013). ByteMutation performs a byte-wise mutation
+on an individual’s chromosome in a Genetic Algorithm.
        
+
        
+
        Parameters:
        
+
          
+
        • mutation_cand (Individual, optional) – The candidate individual to be mutated (default is None).
          • 
+
        • problem (AbstractProblem, optional) – The problem instance that provides the fitness function (default is None).
          • 
+
        
+
        
+
        
+
        
+
        
+__init__(mutation_cand: Individual = None, problem: AbstractProblem = None)[source]
        
+
        Initialize the ByteMutation object.
        
+
        
+
        Parameters:
        
+
          
+
        • mutation_cand (Individual, optional) – The candidate individual to be mutated (default is None).
          • 
+
        • problem (AbstractProblem, optional) – The problem instance that provides the fitness function (default is None).
          • 
+
        
+
        
+
        
+
        
+
+
        
+
        
+mutate() → Individual[source]
        
+
        Perform a byte-wise mutation on the candidate individual.
        
+
        A single byte in one of the candidate’s chromosome’s floating-point numbers is randomly selected
+and either incremented or decremented by 1, wrapping around if necessary.
        
+
        
+
        Returns:
        
+
        A new individual with the mutated chromosome.
        
+
        
+
        Return type:
        
+
        Individual
        
+
        
+
        
+
        
+
+
        
+
+
        
+
        
+
        pycellga.mutation.byte_mutation_random module
        
+
        
+
        
+class pycellga.mutation.byte_mutation_random.ByteMutationRandom(mutation_cand: Individual = None, problem: AbstractProblem = None)[source]
        
+
        Bases: MutationOperator
        
+
        ByteMutationRandom operator defined in (Satman, 2013). ByteMutationRandom performs
+a random byte mutation on an individual’s chromosome in a Genetic Algorithm.
        
+
        
+
        Parameters:
        
+
          
+
        • mutation_cand (Individual, optional) – The candidate individual to be mutated (default is None).
          • 
+
        • problem (AbstractProblem, optional) – The problem instance that provides the fitness function (default is None).
          • 
+
        
+
        
+
        
+
        
+
        
+__init__(mutation_cand: Individual = None, problem: AbstractProblem = None)[source]
        
+
        Initialize the ByteMutationRandom object.
        
+
        
+
        Parameters:
        
+
          
+
        • mutation_cand (Individual, optional) – The candidate individual to be mutated (default is None).
          • 
+
        • problem (AbstractProblem, optional) – The problem instance that provides the fitness function (default is None).
          • 
+
        
+
        
+
        
+
        
+
+
        
+
        
+mutate() → Individual[source]
        
+
        Perform a random byte mutation on the candidate individual.
        
+
        A single byte in one of the candidate’s chromosome’s floating-point numbers is randomly selected
+and mutated to a random value.
        
+
        
+
        Returns:
        
+
        A new individual with the mutated chromosome.
        
+
        
+
        Return type:
        
+
        Individual
        
+
        
+
        
+
        
+
+
        
+
+
        
+
        
+
        pycellga.mutation.float_uniform_mutation module
        
+
        
+
        
+class pycellga.mutation.float_uniform_mutation.FloatUniformMutation(mutation_cand: Individual = None, problem: AbstractProblem = None)[source]
        
+
        Bases: MutationOperator
        
+
        FloatUniformMutation performs a uniform mutation on an individual’s chromosome in a Genetic Algorithm.
        
+
        
+
        Parameters:
        
+
          
+
        • mutation_cand (Individual, optional) – The candidate individual to be mutated (default is None).
          • 
+
        • problem (AbstractProblem, optional) – The problem instance that provides the fitness function (default is None).
          • 
+
        
+
        
+
        
+
        
+
        
+__init__(mutation_cand: Individual = None, problem: AbstractProblem = None)[source]
        
+
        Initialize the FloatUniformMutation object.
        
+
        
+
        Parameters:
        
+
          
+
        • mutation_cand (Individual, optional) – The candidate individual to be mutated (default is None).
          • 
+
        • problem (AbstractProblem, optional) – The problem instance that provides the fitness function (default is None).
          • 
+
        
+
        
+
        
+
        
+
+
        
+
        
+mutate() → Individual[source]
        
+
        Perform a uniform mutation on the candidate individual.
        
+
        Each gene in the candidate’s chromosome is mutated by adding or subtracting a random float uniformly
+sampled from [0, 1]. The mutation is rounded to 5 decimal places.
        
+
        
+
        Returns:
        
+
        A new individual with the mutated chromosome.
        
+
        
+
        Return type:
        
+
        Individual
        
+
        
+
        
+
        
+
+
        
+
+
        
+
        
+
        pycellga.mutation.insertion_mutation module
        
+
        
+
        
+class pycellga.mutation.insertion_mutation.InsertionMutation(mutation_cand: Individual = None, problem: AbstractProblem = None)[source]
        
+
        Bases: MutationOperator
        
+
        InsertionMutation performs an insertion mutation on an individual’s chromosome in a Genetic Algorithm.
        
+
        
+
        Parameters:
        
+
          
+
        • mutation_cand (Individual, optional) – The candidate individual to be mutated (default is None).
          • 
+
        • problem (AbstractProblem, optional) – The problem instance that provides the fitness function (default is None).
          • 
+
        
+
        
+
        
+
        
+
        
+__init__(mutation_cand: Individual = None, problem: AbstractProblem = None)[source]
        
+
        Initialize the InsertionMutation object.
        
+
        
+
        Parameters:
        
+
          
+
        • mutation_cand (Individual, optional) – The candidate individual to be mutated (default is None).
          • 
+
        • problem (AbstractProblem, optional) – The problem instance that provides the fitness function (default is None).
          • 
+
        
+
        
+
        
+
        
+
+
        
+
        
+mutate() → Individual[source]
        
+
        Perform an insertion mutation on the candidate individual.
        
+
        A gene in the candidate’s chromosome is randomly selected and moved to a new position in the chromosome.
        
+
        
+
        Returns:
        
+
        A new individual with the mutated chromosome.
        
+
        
+
        Return type:
        
+
        Individual
        
+
        
+
        
+
        
+
+
        
+
+
        
+
        
+
        pycellga.mutation.mutation_operator module
        
+
        
+
        
+class pycellga.mutation.mutation_operator.MutationOperator[source]
        
+
        Bases: object
        
+
        
+
        
+mutate()[source]
        
+
        
+
+
        
+
+
        
+
        
+
        pycellga.mutation.shuffle_mutation module
        
+
        
+
        
+class pycellga.mutation.shuffle_mutation.ShuffleMutation(mutation_cand: Individual = None, problem: AbstractProblem = None)[source]
        
+
        Bases: MutationOperator
        
+
        ShuffleMutation performs a shuffle mutation on an individual’s chromosome in a Genetic Algorithm.
        
+
        
+
        Parameters:
        
+
          
+
        • mutation_cand (Individual, optional) – The candidate individual to be mutated (default is None).
          • 
+
        • problem (AbstractProblem, optional) – The problem instance that provides the fitness function (default is None).
          • 
+
        
+
        
+
        
+
        
+
        
+__init__(mutation_cand: Individual = None, problem: AbstractProblem = None)[source]
        
+
        Initialize the ShuffleMutation object.
        
+
        
+
        Parameters:
        
+
          
+
        • mutation_cand (Individual, optional) – The candidate individual to be mutated (default is None).
          • 
+
        • problem (AbstractProblem, optional) – The problem instance that provides the fitness function (default is None).
          • 
+
        
+
        
+
        
+
        
+
+
        
+
        
+mutate() → Individual[source]
        
+
        Perform a shuffle mutation on the candidate individual.
        
+
        A subsequence of genes in the candidate’s chromosome is randomly selected and shuffled.
        
+
        
+
        Returns:
        
+
        A new individual with the mutated chromosome.
        
+
        
+
        Return type:
        
+
        Individual
        
+
        
+
        
+
        
+
+
        
+
+
        
+
        
+
        pycellga.mutation.swap_mutation module
        
+
        
+
        
+class pycellga.mutation.swap_mutation.SwapMutation(mutation_cand: Individual = None, problem: AbstractProblem = None)[source]
        
+
        Bases: MutationOperator
        
+
        SwapMutation performs a swap mutation on an individual’s chromosome in a Genetic Algorithm.
        
+
        
+
        Parameters:
        
+
          
+
        • mutation_cand (Individual, optional) – The candidate individual to be mutated (default is None).
          • 
+
        • problem (AbstractProblem, optional) – The problem instance that provides the fitness function (default is None).
          • 
+
        
+
        
+
        
+
        
+
        
+__init__(mutation_cand: Individual = None, problem: AbstractProblem = None)[source]
        
+
        Initialize the SwapMutation object.
        
+
        
+
        Parameters:
        
+
          
+
        • mutation_cand (Individual, optional) – The candidate individual to be mutated (default is None).
          • 
+
        • problem (AbstractProblem, optional) – The problem instance that provides the fitness function (default is None).
          • 
+
        
+
        
+
        
+
        
+
+
        
+
        
+mutate() → Individual[source]
        
+
        Perform a swap mutation on the candidate individual.
        
+
        Two genes in the candidate’s chromosome are randomly selected and swapped.
        
+
        
+
        Returns:
        
+
        A new individual with the mutated chromosome.
        
+
        
+
        Return type:
        
+
        Individual
        
+
        
+
        
+
        
+
+
        
+
+
        
+
        
+
        pycellga.mutation.two_opt_mutation module
        
+
        
+
        
+class pycellga.mutation.two_opt_mutation.TwoOptMutation(mutation_cand: Individual = None, problem: AbstractProblem = None)[source]
        
+
        Bases: MutationOperator
        
+
        TwoOptMutation performs a 2-opt mutation on an individual’s chromosome in a Genetic Algorithm.
        
+
        
+
        Parameters:
        
+
          
+
        • mutation_cand (Individual, optional) – The candidate individual to be mutated (default is None).
          • 
+
        • problem (AbstractProblem, optional) – The problem instance that provides the fitness function (default is None).
          • 
+
        
+
        
+
        
+
        
+
        
+__init__(mutation_cand: Individual = None, problem: AbstractProblem = None)[source]
        
+
        Initialize the TwoOptMutation object.
        
+
        
+
        Parameters:
        
+
          
+
        • mutation_cand (Individual, optional) – The candidate individual to be mutated (default is None).
          • 
+
        • problem (AbstractProblem, optional) – The problem instance that provides the fitness function (default is None).
          • 
+
        
+
        
+
        
+
        
+
+
        
+
        
+mutate() → Individual[source]
        
+
        Perform a 2-opt mutation on the candidate individual.
        
+
        A segment of the candidate’s chromosome is randomly selected and reversed.
        
+
        
+
        Returns:
        
+
        A new individual with the mutated chromosome.
        
+
        
+
        Return type:
        
+
        Individual
        
+
        
+
        
+
        
+
+
        
+
+
        
+
        
+
        Module contents
        
+
        
+
        
+
+
+           
        
+          
        
+          
+        
        
+      
        
+    
        
+  
        
+   
+
+
+
\ No newline at end of file
diff --git a/src.neighborhoods.html b/pycellga.neighborhoods.html
similarity index 58%
rename from src.neighborhoods.html
rename to pycellga.neighborhoods.html
index 97d3e29..69f9fbc 100644
--- a/src.neighborhoods.html
+++ b/pycellga.neighborhoods.html
@@ -4,7 +4,7 @@
   
 
   
-  src.neighborhoods package — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.neighborhoods package — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -21,8 +21,8 @@
     
     
     
-    
-     
+    
+     
 
 
  
@@ -47,24 +47,24 @@
               
        Contents:
        
 
          
 
        • pycellga
            
-
          • src package
              
-
            • Subpackages
@@ -86,10 +86,10 @@
   
   
              
@@ -97,16 +97,16 @@
           
              
            
              
              
-  
              
-
              src.neighborhoods package
              
+  
              
+
              pycellga.neighborhoods package
              
 
              
 
              Submodules
              
 
              
-
              
-
              src.neighborhoods.compact_13 module
              
+
              
+
              pycellga.neighborhoods.compact_13 module
              
 
              
-
              
-class src.neighborhoods.compact_13.Compact13(position, n_rows, n_cols)[source]
              
+
              
+class pycellga.neighborhoods.compact_13.Compact13(position, n_rows, n_cols)[source]
              
 
              Bases: object
              
 
              Compact13 calculates the positions of the 12 neighbors in a 2D grid for a given position,
 considering wrapping at the grid edges.
              
@@ -120,8 +120,8 @@ 
              Submodules
-
              
-__init__(position, n_rows, n_cols)[source]
              
+
              
+__init__(position, n_rows, n_cols)[source]
              
 
              Initialize the Compact13 object.
              
 
              
 
              Parameters:
              
@@ -135,8 +135,8 @@ 
              Submodules
-
              
-calculate_neighbors_positions() → list[source]
              
+
              
+calculate_neighbors_positions() → list[source]
              
 
              Calculate the positions of the 12 neighbors for the given position in the grid.
              
 
              The neighbors are determined by considering wrapping at the grid edges.
              
 
              
@@ -152,11 +152,11 @@ 
              Submodules
-
              src.neighborhoods.compact_21 module
              
+
              
+
              pycellga.neighborhoods.compact_21 module
              
 
              
-
              
-class src.neighborhoods.compact_21.Compact21(position, n_rows, n_cols)[source]
              
+
              
+class pycellga.neighborhoods.compact_21.Compact21(position, n_rows, n_cols)[source]
              
 
              Bases: object
              
 
              Compact21 calculates the positions of the 20 neighbors in a 2D grid for a given position,
 considering wrapping at the grid edges.
              
@@ -170,8 +170,8 @@ 
              Submodules
-
              
-__init__(position, n_rows, n_cols)[source]
              
+
              
+__init__(position, n_rows, n_cols)[source]
              
 
              Initialize the Compact21 object.
              
 
              
 
              Parameters:
              
@@ -185,8 +185,8 @@ 
              Submodules
-
              
-calculate_neighbors_positions() → list[source]
              
+
              
+calculate_neighbors_positions() → list[source]
              
 
              Calculate the positions of the 20 neighbors for the given position in the grid.
              
 
              The neighbors are determined by considering wrapping at the grid edges.
              
 
              
@@ -202,11 +202,11 @@ 
              Submodules
-
              src.neighborhoods.compact_25 module
              
+
              
+
              pycellga.neighborhoods.compact_25 module
              
 
              
-
              
-class src.neighborhoods.compact_25.Compact25(position, n_rows, n_cols)[source]
              
+
              
+class pycellga.neighborhoods.compact_25.Compact25(position, n_rows, n_cols)[source]
              
 
              Bases: object
              
 
              Compact25 calculates the positions of the 24 neighbors in a 2D grid for a given position,
 considering wrapping at the grid edges.
              
@@ -220,8 +220,8 @@ 
              Submodules
-
              
-__init__(position, n_rows, n_cols)[source]
              
+
              
+__init__(position, n_rows, n_cols)[source]
              
 
              Initialize the Compact25 object.
              
 
              
 
              Parameters:
              
@@ -235,8 +235,8 @@ 
              Submodules
-
              
-calculate_neighbors_positions() → list[source]
              
+
              
+calculate_neighbors_positions() → list[source]
              
 
              Calculate the positions of the 24 neighbors for the given position in the grid.
              
 
              The neighbors are determined by considering wrapping at the grid edges.
              
 
              
@@ -252,11 +252,11 @@ 
              Submodules
-
              src.neighborhoods.compact_9 module
              
+
              
+
              pycellga.neighborhoods.compact_9 module
              
 
              
-
              
-class src.neighborhoods.compact_9.Compact9(position, n_rows, n_cols)[source]
              
+
              
+class pycellga.neighborhoods.compact_9.Compact9(position, n_rows, n_cols)[source]
              
 
              Bases: object
              
 
              Compact9 calculates the positions of the 8 neighbors in a 2D grid for a given position,
 considering wrapping at the grid edges.
              
@@ -270,8 +270,8 @@ 
              Submodules
-
              
-__init__(position, n_rows, n_cols)[source]
              
+
              
+__init__(position, n_rows, n_cols)[source]
              
 
              Initialize the Compact9 object.
              
 
              
 
              Parameters:
              
@@ -285,8 +285,8 @@ 
              Submodules
-
              
-calculate_neighbors_positions() → list[source]
              
+
              
+calculate_neighbors_positions() → list[source]
              
 
              Calculate the positions of the 8 neighbors for the given position in the grid.
              
 
              The neighbors are determined by considering wrapping at the grid edges.
              
 
              
@@ -302,11 +302,11 @@ 
              Submodules
-
              src.neighborhoods.linear_5 module
              
+
              
+
              pycellga.neighborhoods.linear_5 module
              
 
              
-
              
-class src.neighborhoods.linear_5.Linear5(position, n_rows, n_cols)[source]
              
+
              
+class pycellga.neighborhoods.linear_5.Linear5(position, n_rows, n_cols)[source]
              
 
              Bases: object
              
 
              Linear5 calculates the positions of the 4 neighbors in a 2D grid for a given position,
 considering wrapping at the grid edges.
              
@@ -320,8 +320,8 @@ 
              Submodules
-
              
-__init__(position, n_rows, n_cols)[source]
              
+
              
+__init__(position, n_rows, n_cols)[source]
              
 
              Initialize the Linear5 object.
              
 
              
 
              Parameters:
              
@@ -335,8 +335,8 @@ 
              Submodules
-
              
-calculate_neighbors_positions() → list[source]
              
+
              
+calculate_neighbors_positions() → list[source]
              
 
              Calculate the positions of the 4 neighbors for the given position in the grid.
              
 
              The neighbors are determined by considering wrapping at the grid edges.
              
 
              
@@ -352,11 +352,11 @@ 
              Submodules
-
              src.neighborhoods.linear_9 module
              
+
              
+
              pycellga.neighborhoods.linear_9 module
              
 
              
-
              
-class src.neighborhoods.linear_9.Linear9(position, n_rows, n_cols)[source]
              
+
              
+class pycellga.neighborhoods.linear_9.Linear9(position, n_rows, n_cols)[source]
              
 
              Bases: object
              
 
              Linear9 calculates the positions of the 8 neighbors in a 2D grid for a given position,
 considering wrapping at the grid edges.
              
@@ -370,8 +370,8 @@ 
              Submodules
-
              
-__init__(position, n_rows, n_cols)[source]
              
+
              
+__init__(position, n_rows, n_cols)[source]
              
 
              Initialize the Linear9 object.
              
 
              
 
              Parameters:
              
@@ -385,8 +385,8 @@ 
              Submodules
-
              
-calculate_neighbors_positions() → list[source]
              
+
              
+calculate_neighbors_positions() → list[source]
              
 
              Calculate the positions of the 8 neighbors for the given position in the grid.
              
 
              The neighbors are determined by considering wrapping at the grid edges.
              
 
              
@@ -402,8 +402,8 @@ 
              Submodules
-
              Module contents
              
+
              
+
              Module contents
              
 
              
 
              
 
@@ -411,8 +411,8 @@ 
              Submodules
-        
-        
+        
+        
     
              
 
   
              
diff --git a/pycellga.problems.html b/pycellga.problems.html
new file mode 100644
index 0000000..f4c806f
--- /dev/null
+++ b/pycellga.problems.html
@@ -0,0 +1,225 @@
+
+
+
+  
+
+  
+  pycellga.problems package — PYCELLGA Documentation 1.0.0 documentation
+      
+      
+
+  
+  
+  
+        
+        
+        
+        
+        
+    
+    
+    
+    
+     
+
+
+ 
+  
              
+    
+
+    
              
+
+      
              
+        
              
+          
              
+  
+  
              
+
              
+          
              
+           
              
+             
+  
              
+
              pycellga.problems package
              
+
              
+
              Subpackages
              
+
              
+
+
              
+
              
+
              
+
              Submodules
              
+
              
+
              
+
              pycellga.problems.abstract_problem module
              
+
              
+
              
+class pycellga.problems.abstract_problem.AbstractProblem[source]
              
+
              Bases: object
              
+
              An abstract base class for optimization problems.
              
+
              
+
              
+f(x)[source]
              
+
              Evaluates the fitness of a given solution x.
              
+
              
+
+
              
+
              
+f(x)[source]
              
+
              Evaluate the fitness of a given solution x.
              
+
              
+
              Parameters:
              
+
              x (list) – A list representing a candidate solution.
              
+
              
+
              Returns:
              
+
              The fitness value of the candidate solution.
              
+
              
+
              Return type:
              
+
              float
              
+
              
+
              Raises:
              
+
              NotImplementedError – If the method is not implemented by a subclass.
              
+
              
+
              
+
              
+
+
              
+
+
              
+
              
+
              Module contents
              
+
              
+
              
+
+
+           
              
+          
              
+          
+        
              
+      
              
+    
              
+  
              
+   
+
+
+
\ No newline at end of file
diff --git a/src.problems.single_objective.continuous.html b/pycellga.problems.single_objective.continuous.html
similarity index 59%
rename from src.problems.single_objective.continuous.html
rename to pycellga.problems.single_objective.continuous.html
index 28e63d5..2d1286e 100644
--- a/src.problems.single_objective.continuous.html
+++ b/pycellga.problems.single_objective.continuous.html
@@ -4,7 +4,7 @@
   
 
   
-  src.problems.single_objective.continuous package — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.problems.single_objective.continuous package — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -21,8 +21,8 @@
     
     
     
-    
-     
+    
+     
 
 
  
@@ -47,24 +47,24 @@
               
              Contents:
              
 
                
 
              • pycellga
                  
-
                • src package
                    
-
                  • Subpackages
@@ -86,12 +86,12 @@
   
   
                    
@@ -99,29 +99,29 @@
           
                    
            
                    
              
-  
                    
-
                    src.problems.single_objective.continuous package
                    
+  
                    
+
                    pycellga.problems.single_objective.continuous package
                    
 
                    
 
                    Submodules
                    
 
                    
-
                    
-
                    src.problems.single_objective.continuous.ackley module
                    
+
                    
+
                    pycellga.problems.single_objective.continuous.ackley module
                    
 
                    
-
                    
-class src.problems.single_objective.continuous.ackley.Ackley[source]
                    
+
                    
+class pycellga.problems.single_objective.continuous.ackley.Ackley[source]
                    
 
                    Bases: AbstractProblem
                    
 
                    Ackley function implementation for optimization problems.
                    
 
                    The Ackley function is widely used for testing optimization algorithms.
 It has a nearly flat outer region and a large hole at the center.
 The function is usually evaluated on the hypercube x_i ∈ [-32.768, 32.768], for all i = 1, 2, …, d.
                    
 
                    
-
                    
-None
                    
+
                    
+None
                    
 
                    
 
 
                    
-
                    
-f(x: list) → float[source]
                    
+
                    
+f(x: list) → float[source]
                    
 
                    Calculates the Ackley function value for a given list of variables.
                    
 
                    
 
@@ -130,7 +130,7 @@ 
                    Submodules
 
                    
-f(x: list) → float[source]
                    
+f(x: list) → float[source]
 
                    Calculate the Ackley function value for a given list of variables.
                    
 
                    
 
                    Parameters:
                    
@@ -148,23 +148,23 @@ 
                    Submodules
-
                    src.problems.single_objective.continuous.bentcigar module
                    
+
                    
+
                    pycellga.problems.single_objective.continuous.bentcigar module
                    
 
                    
-
                    
-class src.problems.single_objective.continuous.bentcigar.Bentcigar[source]
                    
+
                    
+class pycellga.problems.single_objective.continuous.bentcigar.Bentcigar[source]
                    
 
                    Bases: AbstractProblem
                    
 
                    Bentcigar function implementation for optimization problems.
                    
 
                    The Bentcigar function is widely used for testing optimization algorithms.
 The function is usually evaluated on the hypercube x_i ∈ [-100, 100], for all i = 1, 2, …, n.
                    
 
                    
-
                    
-None
                    
+
                    
+None
                    
 
                    
 
 
                    
-
                    
-f(X: list) → float[source]
                    
+
                    
+f(X: list) → float[source]
                    
 
                    Calculates the Bentcigar function value for a given list of variables.
                    
 
                    
 
@@ -173,7 +173,7 @@ 
                    Submodules
 
                    
-f(X: list) → float[source]
                    
+f(X: list) → float[source]
 
                    Calculate the Bentcigar function value for a given list of variables.
                    
 
                    
 
                    Parameters:
                    
@@ -191,23 +191,23 @@ 
                    Submodules
-
                    src.problems.single_objective.continuous.bohachevsky module
                    
+
                    
+
                    pycellga.problems.single_objective.continuous.bohachevsky module
                    
 
                    
-
                    
-class src.problems.single_objective.continuous.bohachevsky.Bohachevsky[source]
                    
+
                    
+class pycellga.problems.single_objective.continuous.bohachevsky.Bohachevsky[source]
                    
 
                    Bases: AbstractProblem
                    
 
                    Bohachevsky function implementation for optimization problems.
                    
 
                    The Bohachevsky function is widely used for testing optimization algorithms.
 The function is usually evaluated on the hypercube x_i ∈ [-15, 15], for all i = 1, 2, …, n.
                    
 
                    
-
                    
-None
                    
+
                    
+None
                    
 
                    
 
 
                    
-
                    
-f(x: list) → float[source]
                    
+
                    
+f(x: list) → float[source]
                    
 
                    Calculates the Bohachevsky function value for a given list of variables.
                    
 
                    
 
@@ -216,7 +216,7 @@ 
                    Submodules
 
                    
-f(x: list) → float[source]
                    
+f(x: list) → float[source]
 
                    Calculate the Bohachevsky function value for a given list of variables.
                    
 
                    
 
                    Parameters:
                    
@@ -234,23 +234,23 @@ 
                    Submodules
-
                    src.problems.single_objective.continuous.chichinadze module
                    
+
                    
+
                    pycellga.problems.single_objective.continuous.chichinadze module
                    
 
                    
-
                    
-class src.problems.single_objective.continuous.chichinadze.Chichinadze[source]
                    
+
                    
+class pycellga.problems.single_objective.continuous.chichinadze.Chichinadze[source]
                    
 
                    Bases: AbstractProblem
                    
 
                    Chichinadze function implementation for optimization problems.
                    
 
                    The Chichinadze function is widely used for testing optimization algorithms.
 The function is usually evaluated on the hypercube x, y ∈ [-30, 30].
                    
 
                    
-
                    
-None
                    
+
                    
+None
                    
 
                    
 
 
                    
-
                    
-f(X: list) → float[source]
                    
+
                    
+f(X: list) → float[source]
                    
 
                    Calculates the Chichinadze function value for a given list of variables.
                    
 
                    
 
@@ -259,7 +259,7 @@ 
                    Submodules
 
                    
-f(X: list) → float[source]
                    
+f(X: list) → float[source]
 
                    Calculate the Chichinadze function value for a given list of variables.
                    
 
                    
 
                    Parameters:
                    
@@ -277,24 +277,24 @@ 
                    Submodules
-
                    src.problems.single_objective.continuous.dropwave module
                    
+
                    
+
                    pycellga.problems.single_objective.continuous.dropwave module
                    
 
                    
-
                    
-class src.problems.single_objective.continuous.dropwave.Dropwave[source]
                    
+
                    
+class pycellga.problems.single_objective.continuous.dropwave.Dropwave[source]
                    
 
                    Bases: AbstractProblem
                    
 
                    Dropwave function for optimization problems.
                    
 
                    The Dropwave function is a multimodal function commonly used as a performance test problem for optimization algorithms.
 It is defined within the bounds -5.12 ≤ xi ≤ 5.12 for i = 1, 2, and has a global minimum at f(0, 0) = -1.
                    
 
                    
-
                    
-f(x: list) → float[source]
                    
+
                    
+f(x: list) → float[source]
                    
 
                    Computes the value of the Dropwave function at a given point x.
                    
 
                    
 
 
                    
 
                    
-f(x: list) → float[source]
                    
+f(x: list) → float[source]
 
                    Evaluate the Dropwave function at a given point.
                    
 
                    
 
                    Parameters:
                    
@@ -326,22 +326,22 @@ 
                    Submodules
-
                    src.problems.single_objective.continuous.fms module
                    
+
                    
+
                    pycellga.problems.single_objective.continuous.fms module
                    
 
                    
-
                    
-class src.problems.single_objective.continuous.fms.Fms[source]
                    
+
                    
+class pycellga.problems.single_objective.continuous.fms.Fms[source]
                    
 
                    Bases: AbstractProblem
                    
 
                    Fms function implementation for optimization problems.
                    
 
                    The Fms function is used for testing optimization algorithms, specifically those dealing with frequency modulation sound.
                    
 
                    
-
                    
-None
                    
+
                    
+None
                    
 
                    
 
 
                    
-
                    
-f(x: list) → float[source]
                    
+
                    
+f(x: list) → float[source]
                    
 
                    Calculates the Fms function value for a given list of variables.
                    
 
                    
 
@@ -352,7 +352,7 @@ 
                    Submodules
 
                    
-f(x: list) → float[source]
                    
+f(x: list) → float[source]
 
                    Calculate the Fms function value for a given list of variables.
                    
 
                    
 
                    Parameters:
                    
@@ -370,18 +370,18 @@ 
                    Submodules
-
                    src.problems.single_objective.continuous.griewank module
                    
+
                    
+
                    pycellga.problems.single_objective.continuous.griewank module
                    
 
                    
-
                    
-class src.problems.single_objective.continuous.griewank.Griewank[source]
                    
+
                    
+class pycellga.problems.single_objective.continuous.griewank.Griewank[source]
                    
 
                    Bases: AbstractProblem
                    
 
                    Griewank function implementation for optimization problems.
                    
 
                    The Griewank function is widely used for testing optimization algorithms.
 The function is usually evaluated on the hypercube x_i ∈ [-600, 600], for all i = 1, 2, …, n.
                    
 
                    
-
                    
-f(X: list) → float[source]
                    
+
                    
+f(X: list) → float[source]
                    
 
                    Calculates the Griewank function value for a given list of variables.
                    
 
                    
 
@@ -390,7 +390,7 @@ 
                    Submodules
 
                    
-f(X: list) → float[source]
                    
+f(X: list) → float[source]
 
                    Calculate the Griewank function value for a given list of variables.
                    
 
                    
 
                    Parameters:
                    
@@ -408,23 +408,23 @@ 
                    Submodules
-
                    src.problems.single_objective.continuous.holzman module
                    
+
                    
+
                    pycellga.problems.single_objective.continuous.holzman module
                    
 
                    
-
                    
-class src.problems.single_objective.continuous.holzman.Holzman[source]
                    
+
                    
+class pycellga.problems.single_objective.continuous.holzman.Holzman[source]
                    
 
                    Bases: AbstractProblem
                    
 
                    Holzman function implementation for optimization problems.
                    
 
                    The Holzman function is widely used for testing optimization algorithms.
 The function is usually evaluated on the hypercube x_i ∈ [-10, 10], for all i = 1, 2, …, n.
                    
 
                    
-
                    
-None
                    
+
                    
+None
                    
 
                    
 
 
                    
-
                    
-f(x: list) → float[source]
                    
+
                    
+f(x: list) → float[source]
                    
 
                    Calculates the Holzman function value for a given list of variables.
                    
 
                    
 
@@ -433,7 +433,7 @@ 
                    Submodules
 
                    
-f(x: list) → float[source]
                    
+f(x: list) → float[source]
 
                    Calculate the Holzman function value for a given list of variables.
                    
 
                    
 
                    Parameters:
                    
@@ -451,23 +451,23 @@ 
                    Submodules
-
                    src.problems.single_objective.continuous.levy module
                    
+
                    
+
                    pycellga.problems.single_objective.continuous.levy module
                    
 
                    
-
                    
-class src.problems.single_objective.continuous.levy.Levy[source]
                    
+
                    
+class pycellga.problems.single_objective.continuous.levy.Levy[source]
                    
 
                    Bases: AbstractProblem
                    
 
                    Levy function implementation for optimization problems.
                    
 
                    The Levy function is widely used for testing optimization algorithms.
 The function is usually evaluated on the hypercube x_i ∈ [-10, 10], for all i = 1, 2, …, n.
                    
 
                    
-
                    
-None
                    
+
                    
+None
                    
 
                    
 
 
                    
-
                    
-f(x: list) → float[source]
                    
+
                    
+f(x: list) → float[source]
                    
 
                    Calculates the Levy function value for a given list of variables.
                    
 
                    
 
@@ -476,7 +476,7 @@ 
                    Submodules
 
                    
-f(x: list) → float[source]
                    
+f(x: list) → float[source]
 
                    Calculate the Levy function value for a given list of variables.
                    
 
                    
 
                    Parameters:
                    
@@ -494,23 +494,23 @@ 
                    Submodules
-
                    src.problems.single_objective.continuous.matyas module
                    
+
                    
+
                    pycellga.problems.single_objective.continuous.matyas module
                    
 
                    
-
                    
-class src.problems.single_objective.continuous.matyas.Matyas[source]
                    
+
                    
+class pycellga.problems.single_objective.continuous.matyas.Matyas[source]
                    
 
                    Bases: AbstractProblem
                    
 
                    Matyas function implementation for optimization problems.
                    
 
                    The Matyas function is widely used for testing optimization algorithms.
 The function is usually evaluated on the hypercube x_i ∈ [-10, 10], for all i = 1, 2, …, n.
                    
 
                    
-
                    
-None
                    
+
                    
+None
                    
 
                    
 
 
                    
-
                    
-f(X: list) → float[source]
                    
+
                    
+f(X: list) → float[source]
                    
 
                    Calculates the Matyas function value for a given list of variables.
                    
 
                    
 
@@ -519,7 +519,7 @@ 
                    Submodules
 
                    
-f(X: list) → float[source]
                    
+f(X: list) → float[source]
 
                    Calculate the Matyas function value for a given list of variables.
                    
 
                    
 
                    Parameters:
                    
@@ -537,23 +537,23 @@ 
                    Submodules
-
                    src.problems.single_objective.continuous.pow module
                    
+
                    
+
                    pycellga.problems.single_objective.continuous.pow module
                    
 
                    
-
                    
-class src.problems.single_objective.continuous.pow.Pow[source]
                    
+
                    
+class pycellga.problems.single_objective.continuous.pow.Pow[source]
                    
 
                    Bases: AbstractProblem
                    
 
                    Pow function implementation for optimization problems.
                    
 
                    The Pow function is widely used for testing optimization algorithms.
 The function is usually evaluated on the hypercube x_i ∈ [-5.0, 15.0].
                    
 
                    
-
                    
-None
                    
+
                    
+None
                    
 
                    
 
 
                    
-
                    
-f(x: list) → float[source]
                    
+
                    
+f(x: list) → float[source]
                    
 
                    Calculates the Pow function value for a given list of variables.
                    
 
                    
 
@@ -562,7 +562,7 @@ 
                    Submodules
 
                    
-f(x: list) → float[source]
                    
+f(x: list) → float[source]
 
                    Calculate the Pow function value for a given list of variables.
                    
 
                    
 
                    Parameters:
                    
@@ -580,23 +580,23 @@ 
                    Submodules
-
                    src.problems.single_objective.continuous.powell module
                    
+
                    
+
                    pycellga.problems.single_objective.continuous.powell module
                    
 
                    
-
                    
-class src.problems.single_objective.continuous.powell.Powell[source]
                    
+
                    
+class pycellga.problems.single_objective.continuous.powell.Powell[source]
                    
 
                    Bases: AbstractProblem
                    
 
                    Powell function implementation for optimization problems.
                    
 
                    The Powell function is widely used for testing optimization algorithms.
 The function is usually evaluated on the hypercube x_i ∈ [-4, 5], for all i = 1, 2, …, n.
                    
 
                    
-
                    
-None
                    
+
                    
+None
                    
 
                    
 
 
                    
-
                    
-f(x: list) → float[source]
                    
+
                    
+f(x: list) → float[source]
                    
 
                    Calculates the Powell function value for a given list of variables.
                    
 
                    
 
@@ -605,7 +605,7 @@ 
                    Submodules
 
                    
-f(x: list) → float[source]
                    
+f(x: list) → float[source]
 
                    Calculate the Powell function value for a given list of variables.
                    
 
                    
 
                    Parameters:
                    
@@ -623,23 +623,23 @@ 
                    Submodules
-
                    src.problems.single_objective.continuous.rastrigin module
                    
+
                    
+
                    pycellga.problems.single_objective.continuous.rastrigin module
                    
 
                    
-
                    
-class src.problems.single_objective.continuous.rastrigin.Rastrigin[source]
                    
+
                    
+class pycellga.problems.single_objective.continuous.rastrigin.Rastrigin[source]
                    
 
                    Bases: AbstractProblem
                    
 
                    Rastrigin function implementation for optimization problems.
                    
 
                    The Rastrigin function is widely used for testing optimization algorithms.
 The function is usually evaluated on the hypercube x_i ∈ [-5.12, 5.12], for all i = 1, 2, …, n.
                    
 
                    
-
                    
-None
                    
+
                    
+None
                    
 
                    
 
 
                    
-
                    
-f(x: list) → float[source]
                    
+
                    
+f(x: list) → float[source]
                    
 
                    Calculates the Rastrigin function value for a given list of variables.
                    
 
                    
 
@@ -648,7 +648,7 @@ 
                    Submodules
 
                    
-f(x: list) → float[source]
                    
+f(x: list) → float[source]
 
                    Calculate the Rastrigin function value for a given list of variables.
                    
 
                    
 
                    Parameters:
                    
@@ -666,23 +666,23 @@ 
                    Submodules
-
                    src.problems.single_objective.continuous.rosenbrock module
                    
+
                    
+
                    pycellga.problems.single_objective.continuous.rosenbrock module
                    
 
                    
-
                    
-class src.problems.single_objective.continuous.rosenbrock.Rosenbrock[source]
                    
+
                    
+class pycellga.problems.single_objective.continuous.rosenbrock.Rosenbrock[source]
                    
 
                    Bases: AbstractProblem
                    
 
                    Rosenbrock function implementation for optimization problems.
                    
 
                    The Rosenbrock function is widely used for testing optimization algorithms.
 The function is usually evaluated on the hypercube x_i ∈ [-5, 10], for all i = 1, 2, …, n.
                    
 
                    
-
                    
-None
                    
+
                    
+None
                    
 
                    
 
 
                    
-
                    
-f(x: list) → float[source]
                    
+
                    
+f(x: list) → float[source]
                    
 
                    Calculates the Rosenbrock function value for a given list of variables.
                    
 
                    
 
@@ -691,7 +691,7 @@ 
                    Submodules
 
                    
-f(x: list) → float[source]
                    
+f(x: list) → float[source]
 
                    Calculate the Rosenbrock function value for a given list of variables.
                    
 
                    
 
                    Parameters:
                    
@@ -709,23 +709,23 @@ 
                    Submodules
-
                    src.problems.single_objective.continuous.rothellipsoid module
                    
+
                    
+
                    pycellga.problems.single_objective.continuous.rothellipsoid module
                    
 
                    
-
                    
-class src.problems.single_objective.continuous.rothellipsoid.Rothellipsoid[source]
                    
+
                    
+class pycellga.problems.single_objective.continuous.rothellipsoid.Rothellipsoid[source]
                    
 
                    Bases: AbstractProblem
                    
 
                    Rotated Hyper-Ellipsoid function implementation for optimization problems.
                    
 
                    The Rotated Hyper-Ellipsoid function is widely used for testing optimization algorithms.
 The function is usually evaluated on the hypercube x_i ∈ [-100, 100], for all i = 1, 2, …, n.
                    
 
                    
-
                    
-None
                    
+
                    
+None
                    
 
                    
 
 
                    
-
                    
-f(x: list) → float[source]
                    
+
                    
+f(x: list) → float[source]
                    
 
                    Calculates the Rotated Hyper-Ellipsoid function value for a given list of variables.
                    
 
                    
 
@@ -734,7 +734,7 @@ 
                    Submodules
 
                    
-f(x: list) → float[source]
                    
+f(x: list) → float[source]
 
                    Calculate the Rotated Hyper-Ellipsoid function value for a given list of variables.
                    
 
                    
 
                    Parameters:
                    
@@ -752,24 +752,24 @@ 
                    Submodules
-
                    src.problems.single_objective.continuous.schaffer module
                    
+
                    
+
                    pycellga.problems.single_objective.continuous.schaffer module
                    
 
                    
-
                    
-class src.problems.single_objective.continuous.schaffer.Schaffer[source]
                    
+
                    
+class pycellga.problems.single_objective.continuous.schaffer.Schaffer[source]
                    
 
                    Bases: AbstractProblem
                    
 
                    Schaffer’s Function.
                    
 
                    This class implements the Schaffer’s function, which is a common benchmark problem for optimization algorithms.
 The function is defined over a multidimensional input and is used to test the performance of optimization methods.
                    
 
                    
-
                    
-f(X: list) → float[source]
                    
+
                    
+f(X: list) → float[source]
                    
 
                    Calculates the value of the Schaffer’s function for a given list of input variables.
                    
 
                    
 
 
                    
 
                    
-f(X: list) → float[source]
                    
+f(X: list) → float[source]
 
                    
 
                    Evaluate the Schaffer’s function at a given point.
                    
 
                    
@@ -800,23 +800,23 @@ 
                    Submodules
-
                    src.problems.single_objective.continuous.schaffer2 module
                    
+
                    
+
                    pycellga.problems.single_objective.continuous.schaffer2 module
                    
 
                    
-
                    
-class src.problems.single_objective.continuous.schaffer2.Schaffer2[source]
                    
+
                    
+class pycellga.problems.single_objective.continuous.schaffer2.Schaffer2[source]
                    
 
                    Bases: AbstractProblem
                    
 
                    Modified Schaffer function #2 implementation for optimization problems.
                    
 
                    The Modified Schaffer function #2 is widely used for testing optimization algorithms.
 The function is usually evaluated on the hypercube x_i ∈ [-100, 100], for all i = 1, 2, …, n.
                    
 
                    
-
                    
-None
                    
+
                    
+None
                    
 
                    
 
 
                    
-
                    
-f(X: list) → float[source]
                    
+
                    
+f(X: list) → float[source]
                    
 
                    Calculates the Modified Schaffer function #2 value for a given list of variables.
                    
 
                    
 
@@ -825,7 +825,7 @@ 
                    Submodules
 
                    
-f(X: list) → float[source]
                    
+f(X: list) → float[source]
 
                    Calculate the Modified Schaffer function #2 value for a given list of variables.
                    
 
                    
 
                    Parameters:
                    
@@ -843,23 +843,23 @@ 
                    Submodules
-
                    src.problems.single_objective.continuous.schwefel module
                    
+
                    
+
                    pycellga.problems.single_objective.continuous.schwefel module
                    
 
                    
-
                    
-class src.problems.single_objective.continuous.schwefel.Schwefel[source]
                    
+
                    
+class pycellga.problems.single_objective.continuous.schwefel.Schwefel[source]
                    
 
                    Bases: AbstractProblem
                    
 
                    Schwefel function implementation for optimization problems.
                    
 
                    The Schwefel function is widely used for testing optimization algorithms.
 The function is usually evaluated on the hypercube x_i ∈ [-500, 500], for all i = 1, 2, …, n.
                    
 
                    
-
                    
-None
                    
+
                    
+None
                    
 
                    
 
 
                    
-
                    
-f(x: list) → float[source]
                    
+
                    
+f(x: list) → float[source]
                    
 
                    Calculates the Schwefel function value for a given list of variables.
                    
 
                    
 
@@ -868,7 +868,7 @@ 
                    Submodules
 
                    
-f(x: list) → float[source]
                    
+f(x: list) → float[source]
 
                    Calculate the Schwefel function value for a given list of variables.
                    
 
                    
 
                    Parameters:
                    
@@ -886,23 +886,23 @@ 
                    Submodules
-
                    src.problems.single_objective.continuous.sphere module
                    
+
                    
+
                    pycellga.problems.single_objective.continuous.sphere module
                    
 
                    
-
                    
-class src.problems.single_objective.continuous.sphere.Sphere[source]
                    
+
                    
+class pycellga.problems.single_objective.continuous.sphere.Sphere[source]
                    
 
                    Bases: AbstractProblem
                    
 
                    Sphere function implementation for optimization problems.
                    
 
                    The Sphere function is widely used for testing optimization algorithms.
 The function is usually evaluated on the hypercube x_i ∈ [-5.12, 5.12], for all i = 1, 2, …, n.
                    
 
                    
-
                    
-None
                    
+
                    
+None
                    
 
                    
 
 
                    
-
                    
-f(x: list) → float[source]
                    
+
                    
+f(x: list) → float[source]
                    
 
                    Calculates the Sphere function value for a given list of variables.
                    
 
                    
 
@@ -911,7 +911,7 @@ 
                    Submodules
 
                    
-f(x: list) → float[source]
                    
+f(x: list) → float[source]
 
                    Calculate the Sphere function value for a given list of variables.
                    
 
                    
 
                    Parameters:
                    
@@ -929,23 +929,23 @@ 
                    Submodules
-
                    src.problems.single_objective.continuous.styblinskitang module
                    
+
                    
+
                    pycellga.problems.single_objective.continuous.styblinskitang module
                    
 
                    
-
                    
-class src.problems.single_objective.continuous.styblinskitang.StyblinskiTang[source]
                    
+
                    
+class pycellga.problems.single_objective.continuous.styblinskitang.StyblinskiTang[source]
                    
 
                    Bases: AbstractProblem
                    
 
                    Styblinski-Tang function implementation for optimization problems.
                    
 
                    The Styblinski-Tang function is widely used for testing optimization algorithms.
 The function is usually evaluated on the hypercube x_i ∈ [-5, 5], for all i = 1, 2, …, n.
                    
 
                    
-
                    
-None
                    
+
                    
+None
                    
 
                    
 
 
                    
-
                    
-f(x: list) → float[source]
                    
+
                    
+f(x: list) → float[source]
                    
 
                    Calculates the Styblinski-Tang function value for a given list of variables.
                    
 
                    
 
@@ -954,7 +954,7 @@ 
                    Submodules
 
                    
-f(x: list) → float[source]
                    
+f(x: list) → float[source]
 
                    Calculate the Styblinski-Tang function value for a given list of variables.
                    
 
                    
 
                    Parameters:
                    
@@ -972,16 +972,16 @@ 
                    Submodules
-
                    src.problems.single_objective.continuous.sumofdifferentpowers module
                    
+
                    
+
                    pycellga.problems.single_objective.continuous.sumofdifferentpowers module
                    
 
                    
-
                    
-class src.problems.single_objective.continuous.sumofdifferentpowers.Sumofdifferentpowers[source]
                    
+
                    
+class pycellga.problems.single_objective.continuous.sumofdifferentpowers.Sumofdifferentpowers[source]
                    
 
                    Bases: AbstractProblem
                    
 
                    Sum of Different Powers function implementation for optimization problems.
                    
 
                    
-
                    
-f(x: list) → float[source]
                    
+
                    
+f(x: list) → float[source]
                    
 
                    Calculate the Sum of Different Powers function value for a given list of variables.
                    
 
                    
 
                    Parameters:
                    
@@ -999,23 +999,23 @@ 
                    Submodules
-
                    src.problems.single_objective.continuous.threehumps module
                    
+
                    
+
                    pycellga.problems.single_objective.continuous.threehumps module
                    
 
                    
-
                    
-class src.problems.single_objective.continuous.threehumps.Threehumps[source]
                    
+
                    
+class pycellga.problems.single_objective.continuous.threehumps.Threehumps[source]
                    
 
                    Bases: AbstractProblem
                    
 
                    Three Hump Camel function implementation for optimization problems.
                    
 
                    The Three Hump Camel function is widely used for testing optimization algorithms.
 The function is usually evaluated on the hypercube x_i ∈ [-5, 5], for all i = 1, 2, …, n.
                    
 
                    
-
                    
-None
                    
+
                    
+None
                    
 
                    
 
 
                    
-
                    
-f(x: list) → float[source]
                    
+
                    
+f(x: list) → float[source]
                    
 
                    Calculates the Three Hump Camel function value for a given list of variables.
                    
 
                    
 
@@ -1024,7 +1024,7 @@ 
                    Submodules
 
                    
-f(x: list) → float[source]
                    
+f(x: list) → float[source]
 
                    Calculate the Three Hump Camel function value for a given list of variables.
                    
 
                    
 
                    Parameters:
                    
@@ -1042,23 +1042,23 @@ 
                    Submodules
-
                    src.problems.single_objective.continuous.zakharov module
                    
+
                    
+
                    pycellga.problems.single_objective.continuous.zakharov module
                    
 
                    
-
                    
-class src.problems.single_objective.continuous.zakharov.Zakharov[source]
                    
+
                    
+class pycellga.problems.single_objective.continuous.zakharov.Zakharov[source]
                    
 
                    Bases: AbstractProblem
                    
 
                    Zakharov function implementation for optimization problems.
                    
 
                    The Zakharov function is widely used for testing optimization algorithms.
 The function is usually evaluated on the hypercube x_i ∈ [-5, 10], for all i = 1, 2, …, n.
                    
 
                    
-
                    
-None
                    
+
                    
+None
                    
 
                    
 
 
                    
-
                    
-f(x: list) → float[source]
                    
+
                    
+f(x: list) → float[source]
                    
 
                    Calculates the Zakharov function value for a given list of variables.
                    
 
                    
 
@@ -1067,7 +1067,7 @@ 
                    Submodules
 
                    
-f(x: list) → float[source]
                    
+f(x: list) → float[source]
 
                    Calculate the Zakharov function value for a given list of variables.
                    
 
                    
 
                    Parameters:
                    
@@ -1085,23 +1085,23 @@ 
                    Submodules
-
                    src.problems.single_objective.continuous.zettle module
                    
+
                    
+
                    pycellga.problems.single_objective.continuous.zettle module
                    
 
                    
-
                    
-class src.problems.single_objective.continuous.zettle.Zettle[source]
                    
+
                    
+class pycellga.problems.single_objective.continuous.zettle.Zettle[source]
                    
 
                    Bases: AbstractProblem
                    
 
                    Zettle function implementation for optimization problems.
                    
 
                    The Zettle function is widely used for testing optimization algorithms.
 The function is usually evaluated on the hypercube x_i ∈ [-5, 5], for all i = 1, 2, …, n.
                    
 
                    
-
                    
-None
                    
+
                    
+None
                    
 
                    
 
 
                    
-
                    
-f(x: list) → float[source]
                    
+
                    
+f(x: list) → float[source]
                    
 
                    Calculates the Zettle function value for a given list of variables.
                    
 
                    
 
@@ -1110,7 +1110,7 @@ 
                    Submodules
 
                    
-f(x: list) → float[source]
                    
+f(x: list) → float[source]
 
                    Calculate the Zettle function value for a given list of variables.
                    
 
                    
 
                    Parameters:
                    
@@ -1128,8 +1128,8 @@ 
                    Submodules
-
                    Module contents
                    
+
                    
+
                    Module contents
                    
 
                    
 
                    
 
@@ -1137,8 +1137,8 @@ 
                    Submodules
-        
-        
+        
+        
     
                    
 
   
                    
diff --git a/src.problems.single_objective.discrete.binary.html b/pycellga.problems.single_objective.discrete.binary.html
similarity index 56%
rename from src.problems.single_objective.discrete.binary.html
rename to pycellga.problems.single_objective.discrete.binary.html
index 23ad7c6..fbea5f7 100644
--- a/src.problems.single_objective.discrete.binary.html
+++ b/pycellga.problems.single_objective.discrete.binary.html
@@ -4,7 +4,7 @@
   
 
   
-  src.problems.single_objective.discrete.binary package — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.problems.single_objective.discrete.binary package — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -21,8 +21,8 @@
     
     
     
-    
-     
+    
+     
 
 
  
@@ -47,24 +47,24 @@
               
                    Contents:
                    
 
                      
 
                    • pycellga
                        
-
                      • src package
                          
-
                        • Subpackages
@@ -86,13 +86,13 @@
   
   
                          
@@ -100,27 +100,27 @@
           
                          
            
                          
              
-  
                          
-
                          src.problems.single_objective.discrete.binary package
                          
+  
                          
+
                          pycellga.problems.single_objective.discrete.binary package
                          
 
                          
 
                          Submodules
                          
 
                          
-
                          
-
                          src.problems.single_objective.discrete.binary.count_sat module
                          
+
                          
+
                          pycellga.problems.single_objective.discrete.binary.count_sat module
                          
 
                          
-
                          
-class src.problems.single_objective.discrete.binary.count_sat.CountSat[source]
                          
+
                          
+class pycellga.problems.single_objective.discrete.binary.count_sat.CountSat[source]
                          
 
                          Bases: AbstractProblem
                          
 
                          CountSat function implementation for optimization problems.
                          
 
                          The CountSat function is used for testing optimization algorithms, particularly those involving satisfiability problems.
                          
 
                          
-
                          
-None
                          
+
                          
+None
                          
 
                          
 
 
                          
-
                          
-f(x: list) → float[source]
                          
+
                          
+f(x: list) → float[source]
                          
 
                          Calculates the CountSat function value for a given list of variables.
                          
 
                          
 
@@ -130,7 +130,7 @@ 
                          Submodules
 
                          
-f(x: list) → float[source]
                          
+f(x: list) → float[source]
 
                          Calculate the CountSat function value for a given list of variables.
                          
 
                          
 
                          Parameters:
                          
@@ -148,23 +148,23 @@ 
                          Submodules
-
                          src.problems.single_objective.discrete.binary.ecc module
                          
+
                          
+
                          pycellga.problems.single_objective.discrete.binary.ecc module
                          
 
                          
-
                          
-class src.problems.single_objective.discrete.binary.ecc.Ecc[source]
                          
+
                          
+class pycellga.problems.single_objective.discrete.binary.ecc.Ecc[source]
                          
 
                          Bases: AbstractProblem
                          
 
                          Error Correcting Codes Design Problem (ECC) function implementation for optimization problems.
                          
 
                          The ECC function is used for testing optimization algorithms, particularly those involving
 error-correcting codes.
                          
 
                          
-
                          
-None
                          
+
                          
+None
                          
 
                          
 
 
                          
-
                          
-f(x: list) → float[source]
                          
+
                          
+f(x: list) → float[source]
                          
 
                          Calculates the ECC function value for a given list of variables.
                          
 
                          
 
@@ -173,7 +173,7 @@ 
                          Submodules
 
                          
-f(x: list) → float[source]
                          
+f(x: list) → float[source]
 
                          Calculate the ECC function value for a given list of variables.
                          
 
                          
 
                          Parameters:
                          
@@ -191,22 +191,22 @@ 
                          Submodules
-
                          src.problems.single_objective.discrete.binary.fms module
                          
+
                          
+
                          pycellga.problems.single_objective.discrete.binary.fms module
                          
 
                          
-
                          
-class src.problems.single_objective.discrete.binary.fms.Fms[source]
                          
+
                          
+class pycellga.problems.single_objective.discrete.binary.fms.Fms[source]
                          
 
                          Bases: AbstractProblem
                          
 
                          Frequency Modulation Sound (FMS) function implementation for optimization problems.
                          
 
                          The FMS function is used for testing optimization algorithms, particularly those involving frequency modulation sound.
                          
 
                          
-
                          
-None
                          
+
                          
+None
                          
 
                          
 
 
                          
-
                          
-f(x: list) → float[source]
                          
+
                          
+f(x: list) → float[source]
                          
 
                          Calculates the FMS function value for a given list of variables.
                          
 
                          
 
@@ -216,7 +216,7 @@ 
                          Submodules
 
                          
-f(x: list) → float[source]
                          
+f(x: list) → float[source]
 
                          Calculate the FMS function value for a given list of variables.
                          
 
                          
 
                          Parameters:
                          
@@ -234,21 +234,21 @@ 
                          Submodules
-
                          src.problems.single_objective.discrete.binary.maxcut100 module
                          
+
                          
+
                          pycellga.problems.single_objective.discrete.binary.maxcut100 module
                          
 
                          
-
                          
-class src.problems.single_objective.discrete.binary.maxcut100.Maxcut100[source]
                          
+
                          
+class pycellga.problems.single_objective.discrete.binary.maxcut100.Maxcut100[source]
                          
 
                          Bases: AbstractProblem
                          
 
                          A class used to represent the Maximum Cut (MAXCUT) function for 100 nodes.
                          
 
                          
-
                          
-None
                          
+
                          
+None
                          
 
                          
 
 
                          
-
                          
-f(x: list) → float[source]
                          
+
                          
+f(x: list) → float[source]
                          
 
                          Calculates the fitness value of a given chromosome.
                          
 
                          
 
@@ -257,7 +257,7 @@ 
                          Submodules
 
                          
-f(x: list) → float[source]
                          
+f(x: list) → float[source]
 
                          Calculates the fitness value of a given chromosome for the Maxcut problem.
                          
 
                          
 
                          Parameters:
                          
@@ -275,22 +275,22 @@ 
                          Submodules
-
                          src.problems.single_objective.discrete.binary.maxcut20_01 module
                          
+
                          
+
                          pycellga.problems.single_objective.discrete.binary.maxcut20_01 module
                          
 
                          
-
                          
-class src.problems.single_objective.discrete.binary.maxcut20_01.Maxcut20_01[source]
                          
+
                          
+class pycellga.problems.single_objective.discrete.binary.maxcut20_01.Maxcut20_01[source]
                          
 
                          Bases: AbstractProblem
                          
 
                          Maximum Cut (MAXCUT) function implementation for optimization problems.
                          
 
                          The MAXCUT function is used for testing optimization algorithms, particularly those involving maximum cut problems.
                          
 
                          
-
                          
-None
                          
+
                          
+None
                          
 
                          
 
 
                          
-
                          
-f(x: list) → float[source]
                          
+
                          
+f(x: list) → float[source]
                          
 
                          Calculates the MAXCUT function value for a given list of variables.
                          
 
                          
 
@@ -299,7 +299,7 @@ 
                          Submodules
 
                          
-f(x: list) → float[source]
                          
+f(x: list) → float[source]
 
                          Calculate the MAXCUT function value for a given list of variables.
                          
 
                          
 
                          Parameters:
                          
@@ -317,22 +317,22 @@ 
                          Submodules
-
                          src.problems.single_objective.discrete.binary.maxcut20_09 module
                          
+
                          
+
                          pycellga.problems.single_objective.discrete.binary.maxcut20_09 module
                          
 
                          
-
                          
-class src.problems.single_objective.discrete.binary.maxcut20_09.Maxcut20_09[source]
                          
+
                          
+class pycellga.problems.single_objective.discrete.binary.maxcut20_09.Maxcut20_09[source]
                          
 
                          Bases: AbstractProblem
                          
 
                          Maximum Cut (MAXCUT) function implementation for optimization problems.
                          
 
                          The MAXCUT function is used for testing optimization algorithms, particularly those involving maximum cut problems.
                          
 
                          
-
                          
-None
                          
+
                          
+None
                          
 
                          
 
 
                          
-
                          
-f(x: list) → float[source]
                          
+
                          
+f(x: list) → float[source]
                          
 
                          Calculates the MAXCUT function value for a given list of variables.
                          
 
                          
 
@@ -341,7 +341,7 @@ 
                          Submodules
 
                          
-f(x: list) → float[source]
                          
+f(x: list) → float[source]
 
                          Calculate the MAXCUT function value for a given list of variables.
                          
 
                          
 
                          Parameters:
                          
@@ -359,24 +359,24 @@ 
                          Submodules
-
                          src.problems.single_objective.discrete.binary.mmdp module
                          
+
                          
+
                          pycellga.problems.single_objective.discrete.binary.mmdp module
                          
 
                          
-
                          
-class src.problems.single_objective.discrete.binary.mmdp.Mmdp[source]
                          
+
                          
+class pycellga.problems.single_objective.discrete.binary.mmdp.Mmdp[source]
                          
 
                          Bases: AbstractProblem
                          
 
                          Represents the Massively Multimodal Deceptive Problem (MMDP).
                          
 
                          The MMDP is designed to deceive genetic algorithms by having multiple local
 optima. The problem is characterized by a chromosome length of 240 and a
 maximum fitness value of 40.
                          
 
                          
-
                          
-None
                          
+
                          
+None
                          
 
                          
 
 
                          
-
                          
-f(x: list) → float[source]
                          
+
                          
+f(x: list) → float[source]
                          
 
                          Evaluates the fitness of a given chromosome.
                          
 
                          
 
@@ -385,7 +385,7 @@ 
                          Submodules
 
                          
-f(x: list) → float[source]
                          
+f(x: list) → float[source]
 
                          Evaluates the fitness of a given chromosome for the MMDP.
                          
 
                          The fitness function is calculated based on the number of ones in each
 of the 40 subproblems, each of length 6.
                          
@@ -407,29 +407,29 @@ 
                          Submodules
-
                          src.problems.single_objective.discrete.binary.one_max module
                          
+
                          
+
                          pycellga.problems.single_objective.discrete.binary.one_max module
                          
 
                          
-
                          
-class src.problems.single_objective.discrete.binary.one_max.OneMax[source]
                          
+
                          
+class pycellga.problems.single_objective.discrete.binary.one_max.OneMax[source]
                          
 
                          Bases: AbstractProblem
                          
 
                          Represents the OneMax problem.
                          
 
                          The OneMax problem is a simple genetic algorithm benchmark problem
 where the fitness of a chromosome is the sum of its bits.
                          
 
                          
-
                          
-None
                          
+
                          
+None
                          
 
                          
 
 
                          
-
                          
-f(x: list) → float[source]
                          
+
                          
+f(x: list) → float[source]
                          
 
                          Evaluates the fitness of a given chromosome.
                          
 
                          
 
 
                          
 
                          
-f(x) → float[source]
                          
+f(x) → float[source]
 
                          Evaluates the fitness of a given chromosome for the OneMax problem.
                          
 
                          The fitness function is the sum of all bits in the chromosome.
                          
 
                          
@@ -449,23 +449,23 @@ 
                          Submodules
-
                          src.problems.single_objective.discrete.binary.peak module
                          
+
                          
+
                          pycellga.problems.single_objective.discrete.binary.peak module
                          
 
                          
-
                          
-class src.problems.single_objective.discrete.binary.peak.Peak[source]
                          
+
                          
+class pycellga.problems.single_objective.discrete.binary.peak.Peak[source]
                          
 
                          Bases: AbstractProblem
                          
 
                          Represents the Peak problem.
                          
 
                          The Peak problem evaluates the fitness of a chromosome based on its
 distance to a set of target peaks.
                          
 
                          
-
                          
-None
                          
+
                          
+None
                          
 
                          
 
 
                          
-
                          
-f(x: list) → float[source]
                          
+
                          
+f(x: list) → float[source]
                          
 
                          Evaluates the fitness of a given chromosome.
                          
 
                          
 
@@ -474,7 +474,7 @@ 
                          Submodules
 
                          
-f(x: list) → float[source]
                          
+f(x: list) → float[source]
 
                          Evaluates the fitness of a given chromosome for the Peak problem.
                          
 
                          The fitness function calculates the distance between the given
 chromosome and a set of randomly generated target peaks.
                          
@@ -495,8 +495,8 @@ 
                          Submodules
-
                          Module contents
                          
+
                          
+
                          Module contents
                          
 
                          
 
                          
 
@@ -504,8 +504,8 @@ 
                          Submodules
-        
-        
+        
+        
     
                          
 
   
                          
diff --git a/pycellga.problems.single_objective.discrete.html b/pycellga.problems.single_objective.discrete.html
new file mode 100644
index 0000000..d65ea01
--- /dev/null
+++ b/pycellga.problems.single_objective.discrete.html
@@ -0,0 +1,247 @@
+
+
+
+  
+
+  
+  pycellga.problems.single_objective.discrete package — PYCELLGA Documentation 1.0.0 documentation
+      
+      
+
+  
+  
+  
+        
+        
+        
+        
+        
+    
+    
+    
+    
+     
+
+
+ 
+  
                          
+    
+
+    
                          
+
+      
                          
+        
                          
+          
                          
+  
+  
                          
+
                          
+          
                          
+           
                          
+             
+  
                          
+
                          pycellga.problems.single_objective.discrete package
                          
+
                          
+
                          Subpackages
                          
+
                          
+
+
                          
+
                          
+
                          
+
                          Module contents
                          
+
                          
+
                          
+
+
+           
                          
+          
                          
+          
+        
                          
+      
                          
+    
                          
+  
                          
+   
+
+
+
\ No newline at end of file
diff --git a/src.problems.single_objective.discrete.permutation.html b/pycellga.problems.single_objective.discrete.permutation.html
similarity index 58%
rename from src.problems.single_objective.discrete.permutation.html
rename to pycellga.problems.single_objective.discrete.permutation.html
index b864528..0495a05 100644
--- a/src.problems.single_objective.discrete.permutation.html
+++ b/pycellga.problems.single_objective.discrete.permutation.html
@@ -4,7 +4,7 @@
   
 
   
-  src.problems.single_objective.discrete.permutation package — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.problems.single_objective.discrete.permutation package — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -21,8 +21,8 @@
     
     
     
-    
-     
+    
+     
 
 
  
@@ -47,24 +47,24 @@
               
                          Contents:
                          
 
                            
 
                          • pycellga
                              
-
                            • src package
                                
-
                              • Subpackages
@@ -86,13 +86,13 @@
   
   
                                
@@ -100,16 +100,16 @@
           
                                
            
                                
              
-  
                                
-
                                src.problems.single_objective.discrete.permutation package
                                
+  
                                
+
                                pycellga.problems.single_objective.discrete.permutation package
                                
 
                                
 
                                Submodules
                                
 
                                
-
                                
-
                                src.problems.single_objective.discrete.permutation.tsp module
                                
+
                                
+
                                pycellga.problems.single_objective.discrete.permutation.tsp module
                                
 
                                
-
                                
-class src.problems.single_objective.discrete.permutation.tsp.Tsp[source]
                                
+
                                
+class pycellga.problems.single_objective.discrete.permutation.tsp.Tsp[source]
                                
 
                                Bases: AbstractProblem
                                
 
                                Represents the Traveling Salesman Problem (TSP).
                                
 
                                This class solves the TSP using geographical distances (GEO) for node coordinates.
                                
@@ -121,8 +121,8 @@ 
                                Submodules
-
                                
-euclidean_dist(a: list, b: list) → float[source]
                                
+
                                
+euclidean_dist(a: list, b: list) → float[source]
                                
 
                                Computes the Euclidean distance between two nodes.
                                
 
                                
 
                                Parameters:
                                
@@ -141,8 +141,8 @@ 
                                Submodules
-
                                
-f(x: list) → float[source]
                                
+
                                
+f(x: list) → float[source]
                                
 
                                Evaluates the fitness of a given chromosome (route) for the TSP.
                                
 
                                This method calculates the total distance of the given route using geographical distances.
                                
 
                                
@@ -159,8 +159,8 @@ 
                                Submodules
-
                                
-gographical_dist(a: list, b: list) → float[source]
                                
+
                                
+gographical_dist(a: list, b: list) → float[source]
                                
 
                                Computes the geographical distance between two nodes using the geodesic distance.
                                
 
                                
 
                                Parameters:
                                
@@ -181,8 +181,8 @@ 
                                Submodules
-
                                Module contents
                                
+
                                
+
                                Module contents
                                
 
                                
 
                                
 
@@ -190,8 +190,8 @@ 
                                Submodules
-        
-        
+        
+        
     
                                
 
   
                                
diff --git a/pycellga.problems.single_objective.html b/pycellga.problems.single_objective.html
new file mode 100644
index 0000000..f4549f4
--- /dev/null
+++ b/pycellga.problems.single_objective.html
@@ -0,0 +1,389 @@
+
+
+
+  
+
+  
+  pycellga.problems.single_objective package — PYCELLGA Documentation 1.0.0 documentation
+      
+      
+
+  
+  
+  
+        
+        
+        
+        
+        
+    
+    
+    
+    
+     
+
+
+ 
+  
                                
+    
+
+    
                                
+
+      
                                
+        
                                
+          
                                
+  
+  
                                
+
                                
+          
                                
+           
                                
+             
+  
                                
+
                                pycellga.problems.single_objective package
                                
+
                                
+
                                Subpackages
                                
+
                                
+
+
                                
+
                                
+
                                
+
                                Module contents
                                
+
                                
+
                                
+
+
+           
                                
+          
                                
+          
+        
                                
+      
                                
+    
                                
+  
                                
+   
+
+
+
\ No newline at end of file
diff --git a/src.recombination.html b/pycellga.recombination.html
similarity index 52%
rename from src.recombination.html
rename to pycellga.recombination.html
index 0c9947e..d2a98b9 100644
--- a/src.recombination.html
+++ b/pycellga.recombination.html
@@ -4,7 +4,7 @@
   
 
   
-  src.recombination package — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.recombination package — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -21,8 +21,8 @@
     
     
     
-    
-     
+    
+     
 
 
  
@@ -47,24 +47,24 @@
               
                                Contents:
                                
 
                                  
 
                                • pycellga
                                    
-
                                  • src package
                                      
-
                                    • Subpackages
@@ -86,10 +86,10 @@
   
   
                                      
@@ -97,16 +97,16 @@
           
                                      
            
                                      
              
-  
                                      
-
                                      src.recombination package
                                      
+  
                                      
+
                                      pycellga.recombination package
                                      
 
                                      
 
                                      Submodules
                                      
 
                                      
-
                                      
-
                                      src.recombination.arithmetic_crossover module
                                      
+
                                      
+
                                      pycellga.recombination.arithmetic_crossover module
                                      
 
                                      
-
                                      
-class src.recombination.arithmetic_crossover.ArithmeticCrossover(parents: list, problem: AbstractProblem)[source]
                                      
+
                                      
+class pycellga.recombination.arithmetic_crossover.ArithmeticCrossover(parents: list, problem: AbstractProblem)[source]
                                      
 
                                      Bases: RecombinationOperator
                                      
 
                                      ArithmeticCrossover performs an arithmetic crossover operation on a pair of parent individuals
 to produce offspring individuals.
                                      
@@ -114,27 +114,27 @@ 
                                      SubmodulesParameters:
 
                                      
 
                                      
 
                                      
 
                                      
-
                                      
-__init__(parents: list, problem: AbstractProblem)[source]
                                      
+
                                      
+__init__(parents: list, problem: AbstractProblem)[source]
                                      
 
                                      Initialize the ArithmeticCrossover object.
                                      
 
                                      
 
                                      Parameters:
                                      
 
                                        
 
                                      • parents (list) – A list containing two parent individuals.
                                        • 
-
                                      • problem (AbstractProblem) – The problem instance that provides the fitness function.
                                        • 
+
                                      • problem (AbstractProblem) – The problem instance that provides the fitness function.
                                        • 
 
                                      
 
                                      
 
                                      
 
                                      
 
 
                                      
-
                                      
-get_recombinations() → List[Individual][source]
                                      
+
                                      
+get_recombinations() → List[Individual][source]
                                      
 
                                      Perform the arithmetic crossover on the parent individuals to produce offspring.
                                      
 
                                      
 
                                      Returns:
                                      
@@ -149,11 +149,11 @@ 
                                      Submodules
-
                                      src.recombination.blxalpha_crossover module
                                      
+
                                      
+
                                      pycellga.recombination.blxalpha_crossover module
                                      
 
                                      
-
                                      
-class src.recombination.blxalpha_crossover.BlxalphaCrossover(parents: list, problem: AbstractProblem)[source]
                                      
+
                                      
+class pycellga.recombination.blxalpha_crossover.BlxalphaCrossover(parents: list, problem: AbstractProblem)[source]
                                      
 
                                      Bases: RecombinationOperator
                                      
 
                                      BlxalphaCrossover performs BLX-alpha crossover on a pair of parent individuals
 to produce offspring individuals.
                                      
@@ -161,27 +161,27 @@ 
                                      SubmodulesParameters:
 
                                      
 
                                      
 
                                      
 
                                      
-
                                      
-__init__(parents: list, problem: AbstractProblem)[source]
                                      
+
                                      
+__init__(parents: list, problem: AbstractProblem)[source]
                                      
 
                                      Initialize the BlxalphaCrossover object.
                                      
 
                                      
 
                                      Parameters:
                                      
 
                                        
 
                                      • parents (list) – A list containing two parent individuals.
                                        • 
-
                                      • problem (AbstractProblem) – The problem instance that provides the fitness function.
                                        • 
+
                                      • problem (AbstractProblem) – The problem instance that provides the fitness function.
                                        • 
 
                                      
 
                                      
 
                                      
 
                                      
 
 
                                      
-
                                      
-combine(p1: Individual, p2: Individual, locationsource: Individual) → Individual[source]
                                      
+
                                      
+combine(p1: Individual, p2: Individual, locationsource: Individual) → Individual[source]
                                      
 
                                      Combine two parent individuals using BLX-alpha crossover to produce a single offspring.
                                      
 
                                      
 
                                      Parameters:
                                      
@@ -201,8 +201,8 @@ 
                                      Submodules
-
                                      
-get_recombinations() → List[Individual][source]
                                      
+
                                      
+get_recombinations() → List[Individual][source]
                                      
 
                                      Perform the BLX-alpha crossover on the parent individuals to produce offspring.
                                      
 
                                      
 
                                      Returns:
                                      
@@ -217,11 +217,11 @@ 
                                      Submodules
-
                                      src.recombination.byte_one_point_crossover module
                                      
+
                                      
+
                                      pycellga.recombination.byte_one_point_crossover module
                                      
 
                                      
-
                                      
-class src.recombination.byte_one_point_crossover.ByteOnePointCrossover(parents: list, problem: AbstractProblem)[source]
                                      
+
                                      
+class pycellga.recombination.byte_one_point_crossover.ByteOnePointCrossover(parents: list, problem: AbstractProblem)[source]
                                      
 
                                      Bases: RecombinationOperator
                                      
 
                                      ByteOnePointCrossover operator defined in (Satman, 2013). ByteOnePointCrossover performs a
 one-point crossover at the byte level on a pair of parent individuals to produce offspring individuals.
                                      
@@ -229,27 +229,27 @@ 
                                      SubmodulesParameters:
 
                                      
 
                                      
 
                                      
 
                                      
-
                                      
-__init__(parents: list, problem: AbstractProblem)[source]
                                      
+
                                      
+__init__(parents: list, problem: AbstractProblem)[source]
                                      
 
                                      Initialize the ByteOnePointCrossover object.
                                      
 
                                      
 
                                      Parameters:
                                      
 
                                        
 
                                      • parents (list) – A list containing two parent individuals.
                                        • 
-
                                      • problem (AbstractProblem) – The problem instance that provides the fitness function.
                                        • 
+
                                      • problem (AbstractProblem) – The problem instance that provides the fitness function.
                                        • 
 
                                      
 
                                      
 
                                      
 
                                      
 
 
                                      
-
                                      
-get_recombinations() → List[Individual][source]
                                      
+
                                      
+get_recombinations() → List[Individual][source]
                                      
 
                                      Perform the one-point crossover on the parent individuals at the byte level to produce offspring.
                                      
 
                                      
 
                                      Returns:
                                      
@@ -264,11 +264,11 @@ 
                                      Submodules
-
                                      src.recombination.byte_uniform_crossover module
                                      
+
                                      
+
                                      pycellga.recombination.byte_uniform_crossover module
                                      
 
                                      
-
                                      
-class src.recombination.byte_uniform_crossover.ByteUniformCrossover(parents: list, problem: AbstractProblem)[source]
                                      
+
                                      
+class pycellga.recombination.byte_uniform_crossover.ByteUniformCrossover(parents: list, problem: AbstractProblem)[source]
                                      
 
                                      Bases: RecombinationOperator
                                      
 
                                      ByteUniformCrossover operator defined in (Satman, 2013). ByteUniformCrossover performs a
 uniform crossover at the byte level on a pair of parent individuals to produce offspring individuals.
                                      
@@ -276,27 +276,27 @@ 
                                      SubmodulesParameters:
 
                                      
 
                                      
 
                                      
 
                                      
-
                                      
-__init__(parents: list, problem: AbstractProblem)[source]
                                      
+
                                      
+__init__(parents: list, problem: AbstractProblem)[source]
                                      
 
                                      Initialize the ByteUniformCrossover object.
                                      
 
                                      
 
                                      Parameters:
                                      
 
                                        
 
                                      • parents (list) – A list containing two parent individuals.
                                        • 
-
                                      • problem (AbstractProblem) – The problem instance that provides the fitness function.
                                        • 
+
                                      • problem (AbstractProblem) – The problem instance that provides the fitness function.
                                        • 
 
                                      
 
                                      
 
                                      
 
                                      
 
 
                                      
-
                                      
-combine(p1: Individual, p2: Individual, locationsource: Individual) → Individual[source]
                                      
+
                                      
+combine(p1: Individual, p2: Individual, locationsource: Individual) → Individual[source]
                                      
 
                                      Combine two parent individuals using uniform crossover at the byte level to produce a single offspring.
                                      
 
                                      
 
                                      Parameters:
                                      
@@ -316,8 +316,8 @@ 
                                      Submodules
-
                                      
-get_recombinations() → List[Individual][source]
                                      
+
                                      
+get_recombinations() → List[Individual][source]
                                      
 
                                      Perform the uniform crossover on the parent individuals to produce offspring.
                                      
 
                                      
 
                                      Returns:
                                      
@@ -332,11 +332,11 @@ 
                                      Submodules
-
                                      src.recombination.flat_crossover module
                                      
+
                                      
+
                                      pycellga.recombination.flat_crossover module
                                      
 
                                      
-
                                      
-class src.recombination.flat_crossover.FlatCrossover(parents: list, problem: AbstractProblem)[source]
                                      
+
                                      
+class pycellga.recombination.flat_crossover.FlatCrossover(parents: list, problem: AbstractProblem)[source]
                                      
 
                                      Bases: RecombinationOperator
                                      
 
                                      FlatCrossover performs a flat crossover on a pair of parent individuals
 to produce offspring individuals.
                                      
@@ -344,27 +344,27 @@ 
                                      SubmodulesParameters:
 
                                      
 
                                      
 
                                      
 
                                      
-
                                      
-__init__(parents: list, problem: AbstractProblem)[source]
                                      
+
                                      
+__init__(parents: list, problem: AbstractProblem)[source]
                                      
 
                                      Initialize the FlatCrossover object.
                                      
 
                                      
 
                                      Parameters:
                                      
 
                                        
 
                                      • parents (list) – A list containing two parent individuals.
                                        • 
-
                                      • problem (AbstractProblem) – The problem instance that provides the fitness function.
                                        • 
+
                                      • problem (AbstractProblem) – The problem instance that provides the fitness function.
                                        • 
 
                                      
 
                                      
 
                                      
 
                                      
 
 
                                      
-
                                      
-combine(p1: Individual, p2: Individual, locationsource: Individual) → Individual[source]
                                      
+
                                      
+combine(p1: Individual, p2: Individual, locationsource: Individual) → Individual[source]
                                      
 
                                      Combine two parent individuals using flat crossover to produce a single offspring.
                                      
 
                                      
 
                                      Parameters:
                                      
@@ -384,8 +384,8 @@ 
                                      Submodules
-
                                      
-get_recombinations() → List[Individual][source]
                                      
+
                                      
+get_recombinations() → List[Individual][source]
                                      
 
                                      Perform the flat crossover on the parent individuals to produce offspring.
                                      
 
                                      
 
                                      Returns:
                                      
@@ -400,11 +400,11 @@ 
                                      Submodules
-
                                      src.recombination.linear_crossover module
                                      
+
                                      
+
                                      pycellga.recombination.linear_crossover module
                                      
 
                                      
-
                                      
-class src.recombination.linear_crossover.LinearCrossover(parents: list, problem: AbstractProblem)[source]
                                      
+
                                      
+class pycellga.recombination.linear_crossover.LinearCrossover(parents: list, problem: AbstractProblem)[source]
                                      
 
                                      Bases: RecombinationOperator
                                      
 
                                      LinearCrossover performs a linear crossover on a pair of parent individuals
 to produce offspring individuals.
                                      
@@ -412,27 +412,27 @@ 
                                      SubmodulesParameters:
 
                                      
 
                                      
 
                                      
 
                                      
-
                                      
-__init__(parents: list, problem: AbstractProblem)[source]
                                      
+
                                      
+__init__(parents: list, problem: AbstractProblem)[source]
                                      
 
                                      Initialize the LinearCrossover object.
                                      
 
                                      
 
                                      Parameters:
                                      
 
                                        
 
                                      • parents (list) – A list containing two parent individuals.
                                        • 
-
                                      • problem (AbstractProblem) – The problem instance that provides the fitness function.
                                        • 
+
                                      • problem (AbstractProblem) – The problem instance that provides the fitness function.
                                        • 
 
                                      
 
                                      
 
                                      
 
                                      
 
 
                                      
-
                                      
-combine(p1: Individual, p2: Individual, locationsource: Individual) → Individual[source]
                                      
+
                                      
+combine(p1: Individual, p2: Individual, locationsource: Individual) → Individual[source]
                                      
 
                                      Combine two parent individuals using linear crossover to produce a single offspring.
                                      
 
                                      
 
                                      Parameters:
                                      
@@ -452,8 +452,8 @@ 
                                      Submodules
-
                                      
-get_recombinations() → List[Individual][source]
                                      
+
                                      
+get_recombinations() → List[Individual][source]
                                      
 
                                      Perform the linear crossover on the parent individuals to produce offspring.
                                      
 
                                      
 
                                      Returns:
                                      
@@ -468,11 +468,11 @@ 
                                      Submodules
-
                                      src.recombination.one_point_crossover module
                                      
+
                                      
+
                                      pycellga.recombination.one_point_crossover module
                                      
 
                                      
-
                                      
-class src.recombination.one_point_crossover.OnePointCrossover(parents: list, problem: AbstractProblem)[source]
                                      
+
                                      
+class pycellga.recombination.one_point_crossover.OnePointCrossover(parents: list, problem: AbstractProblem)[source]
                                      
 
                                      Bases: RecombinationOperator
                                      
 
                                      OnePointCrossover performs a one-point crossover on a pair of parent individuals
 to produce offspring individuals.
                                      
@@ -480,27 +480,27 @@ 
                                      SubmodulesParameters:
 
                                      
 
                                      
 
                                      
 
                                      
-
                                      
-__init__(parents: list, problem: AbstractProblem)[source]
                                      
+
                                      
+__init__(parents: list, problem: AbstractProblem)[source]
                                      
 
                                      Initialize the OnePointCrossover object.
                                      
 
                                      
 
                                      Parameters:
                                      
 
                                        
 
                                      • parents (list) – A list containing two parent individuals.
                                        • 
-
                                      • problem (AbstractProblem) – The problem instance that provides the fitness function.
                                        • 
+
                                      • problem (AbstractProblem) – The problem instance that provides the fitness function.
                                        • 
 
                                      
 
                                      
 
                                      
 
                                      
 
 
                                      
-
                                      
-get_recombinations() → List[Individual][source]
                                      
+
                                      
+get_recombinations() → List[Individual][source]
                                      
 
                                      Perform the one-point crossover on the parent individuals to produce offspring.
                                      
 
                                      
 
                                      Returns:
                                      
@@ -515,11 +515,11 @@ 
                                      Submodules
-
                                      src.recombination.pmx_crossover module
                                      
+
                                      
+
                                      pycellga.recombination.pmx_crossover module
                                      
 
                                      
-
                                      
-class src.recombination.pmx_crossover.PMXCrossover(parents: list, problem: AbstractProblem)[source]
                                      
+
                                      
+class pycellga.recombination.pmx_crossover.PMXCrossover(parents: list, problem: AbstractProblem)[source]
                                      
 
                                      Bases: RecombinationOperator
                                      
 
                                      PMXCrossover performs Partially Mapped Crossover (PMX) on a pair of parent individuals
 to produce offspring individuals.
                                      
@@ -527,27 +527,27 @@ 
                                      SubmodulesParameters:
 
                                      
 
                                      
 
                                      
 
                                      
-
                                      
-__init__(parents: list, problem: AbstractProblem)[source]
                                      
+
                                      
+__init__(parents: list, problem: AbstractProblem)[source]
                                      
 
                                      Initialize the PMXCrossover object.
                                      
 
                                      
 
                                      Parameters:
                                      
 
                                        
 
                                      • parents (list) – A list containing two parent individuals.
                                        • 
-
                                      • problem (AbstractProblem) – The problem instance that provides the fitness function.
                                        • 
+
                                      • problem (AbstractProblem) – The problem instance that provides the fitness function.
                                        • 
 
                                      
 
                                      
 
                                      
 
                                      
 
 
                                      
-
                                      
-get_recombinations() → List[Individual][source]
                                      
+
                                      
+get_recombinations() → List[Individual][source]
                                      
 
                                      Perform the PMX crossover on the parent individuals to produce offspring.
                                      
 
                                      
 
                                      Returns:
                                      
@@ -562,25 +562,25 @@ 
                                      Submodules
-
                                      src.recombination.recombination_operator module
                                      
+
                                      
+
                                      pycellga.recombination.recombination_operator module
                                      
 
                                      
-
                                      
-class src.recombination.recombination_operator.RecombinationOperator[source]
                                      
+
                                      
+class pycellga.recombination.recombination_operator.RecombinationOperator[source]
                                      
 
                                      Bases: object
                                      
 
                                      
-
                                      
-get_recombinations() → list[source]
                                      
+
                                      
+get_recombinations() → list[source]
                                      
 
                                      
 
 
                                      
 
 
                                      
-
                                      
-
                                      src.recombination.two_point_crossover module
                                      
+
                                      
+
                                      pycellga.recombination.two_point_crossover module
                                      
 
                                      
-
                                      
-class src.recombination.two_point_crossover.TwoPointCrossover(parents: list, problem: AbstractProblem)[source]
                                      
+
                                      
+class pycellga.recombination.two_point_crossover.TwoPointCrossover(parents: list, problem: AbstractProblem)[source]
                                      
 
                                      Bases: RecombinationOperator
                                      
 
                                      TwoPointCrossover performs a two-point crossover on a pair of parent individuals
 to produce offspring individuals.
                                      
@@ -588,27 +588,27 @@ 
                                      SubmodulesParameters:
 
                                      
 
                                      
 
                                      
 
                                      
-
                                      
-__init__(parents: list, problem: AbstractProblem)[source]
                                      
+
                                      
+__init__(parents: list, problem: AbstractProblem)[source]
                                      
 
                                      Initialize the TwoPointCrossover object.
                                      
 
                                      
 
                                      Parameters:
                                      
 
                                        
 
                                      • parents (list) – A list containing two parent individuals.
                                        • 
-
                                      • problem (AbstractProblem) – The problem instance that provides the fitness function.
                                        • 
+
                                      • problem (AbstractProblem) – The problem instance that provides the fitness function.
                                        • 
 
                                      
 
                                      
 
                                      
 
                                      
 
 
                                      
-
                                      
-get_recombinations() → List[Individual][source]
                                      
+
                                      
+get_recombinations() → List[Individual][source]
                                      
 
                                      Perform the two-point crossover on the parent individuals to produce offspring.
                                      
 
                                      
 
                                      Returns:
                                      
@@ -623,11 +623,11 @@ 
                                      Submodules
-
                                      src.recombination.unfair_avarage_crossover module
                                      
+
                                      
+
                                      pycellga.recombination.unfair_avarage_crossover module
                                      
 
                                      
-
                                      
-class src.recombination.unfair_avarage_crossover.UnfairAvarageCrossover(parents: list, problem: AbstractProblem)[source]
                                      
+
                                      
+class pycellga.recombination.unfair_avarage_crossover.UnfairAvarageCrossover(parents: list, problem: AbstractProblem)[source]
                                      
 
                                      Bases: RecombinationOperator
                                      
 
                                      UnfairAvarageCrossover performs an unfair average crossover on a pair of parent individuals
 to produce offspring individuals.
                                      
@@ -635,27 +635,27 @@ 
                                      SubmodulesParameters:
 
                                      
 
                                      
 
                                      
 
                                      
-
                                      
-__init__(parents: list, problem: AbstractProblem)[source]
                                      
+
                                      
+__init__(parents: list, problem: AbstractProblem)[source]
                                      
 
                                      Initialize the UnfairAvarageCrossover object.
                                      
 
                                      
 
                                      Parameters:
                                      
 
                                        
 
                                      • parents (list) – A list containing two parent individuals.
                                        • 
-
                                      • problem (AbstractProblem) – The problem instance that provides the fitness function.
                                        • 
+
                                      • problem (AbstractProblem) – The problem instance that provides the fitness function.
                                        • 
 
                                      
 
                                      
 
                                      
 
                                      
 
 
                                      
-
                                      
-get_recombinations() → List[Individual][source]
                                      
+
                                      
+get_recombinations() → List[Individual][source]
                                      
 
                                      Perform the unfair average crossover on the parent individuals to produce offspring.
                                      
 
                                      
 
                                      Returns:
                                      
@@ -670,11 +670,11 @@ 
                                      Submodules
-
                                      src.recombination.uniform_crossover module
                                      
+
                                      
+
                                      pycellga.recombination.uniform_crossover module
                                      
 
                                      
-
                                      
-class src.recombination.uniform_crossover.UniformCrossover(parents: list, problem: AbstractProblem)[source]
                                      
+
                                      
+class pycellga.recombination.uniform_crossover.UniformCrossover(parents: list, problem: AbstractProblem)[source]
                                      
 
                                      Bases: RecombinationOperator
                                      
 
                                      UniformCrossover performs a uniform crossover on a pair of parent individuals
 to produce offspring individuals.
                                      
@@ -682,27 +682,27 @@ 
                                      SubmodulesParameters:
 
                                      
 
                                      
 
                                      
 
                                      
-
                                      
-__init__(parents: list, problem: AbstractProblem)[source]
                                      
+
                                      
+__init__(parents: list, problem: AbstractProblem)[source]
                                      
 
                                      Initialize the UniformCrossover object.
                                      
 
                                      
 
                                      Parameters:
                                      
 
                                        
 
                                      • parents (list) – A list containing two parent individuals.
                                        • 
-
                                      • problem (AbstractProblem) – The problem instance that provides the fitness function.
                                        • 
+
                                      • problem (AbstractProblem) – The problem instance that provides the fitness function.
                                        • 
 
                                      
 
                                      
 
                                      
 
                                      
 
 
                                      
-
                                      
-combine(p1: Individual, p2: Individual, locationsource: Individual) → Individual[source]
                                      
+
                                      
+combine(p1: Individual, p2: Individual, locationsource: Individual) → Individual[source]
                                      
 
                                      Combine two parent individuals using uniform crossover to produce a single offspring.
                                      
 
                                      
 
                                      Parameters:
                                      
@@ -722,8 +722,8 @@ 
                                      Submodules
-
                                      
-get_recombinations() → List[Individual][source]
                                      
+
                                      
+get_recombinations() → List[Individual][source]
                                      
 
                                      Perform the uniform crossover on the parent individuals to produce offspring.
                                      
 
                                      
 
                                      Returns:
                                      
@@ -738,8 +738,8 @@ 
                                      Submodules
-
                                      Module contents
                                      
+
                                      
+
                                      Module contents
                                      
 
                                      
 
                                      
 
@@ -747,8 +747,8 @@ 
                                      Submodules
-        
-        
+        
+        
     
                                      
 
   
                                      
diff --git a/src.selection.html b/pycellga.selection.html
similarity index 53%
rename from src.selection.html
rename to pycellga.selection.html
index a8f9429..5646246 100644
--- a/src.selection.html
+++ b/pycellga.selection.html
@@ -4,7 +4,7 @@
   
 
   
-  src.selection package — PYCELLGA Documentation 1.0.0 documentation
+  pycellga.selection package — PYCELLGA Documentation 1.0.0 documentation
       
       
 
@@ -21,8 +21,8 @@
     
     
     
-    
-     
+    
+     
 
 
  
@@ -47,24 +47,24 @@
               
                                      Contents:
                                      
 
                                        
 
                                      • pycellga
                                          
-
                                        • src package
                                            
-
                                          • Subpackages
@@ -86,10 +86,10 @@
   
   
                                            
@@ -97,16 +97,16 @@
           
                                            
            
                                            
              
-  
                                            
-
                                            src.selection package
                                            
+  
                                            
+
                                            pycellga.selection package
                                            
 
                                            
 
                                            Submodules
                                            
 
                                            
-
                                            
-
                                            src.selection.roulette_wheel_selection module
                                            
+
                                            
+
                                            pycellga.selection.roulette_wheel_selection module
                                            
 
                                            
-
                                            
-class src.selection.roulette_wheel_selection.RouletteWheelSelection(pop_list: List[Individual] = [], c: int = 0)[source]
                                            
+
                                            
+class pycellga.selection.roulette_wheel_selection.RouletteWheelSelection(pop_list: List[Individual] = [], c: int = 0)[source]
                                            
 
                                            Bases: SelectionOperator
                                            
 
                                            RouletteWheelSelection performs a roulette wheel selection on a population of individuals
 to select parent individuals for crossover.
                                            
@@ -119,8 +119,8 @@ 
                                            Submodules
-
                                            
-__init__(pop_list: List[Individual] = [], c: int = 0)[source]
                                            
+
                                            
+__init__(pop_list: List[Individual] = [], c: int = 0)[source]
                                            
 
                                            Initialize the RouletteWheelSelection object.
                                            
 
                                            
 
                                            Parameters:
                                            
@@ -133,8 +133,8 @@ 
                                            Submodules
-
                                            
-get_parents() → List[Individual][source]
                                            
+
                                            
+get_parents() → List[Individual][source]
                                            
 
                                            Perform the roulette wheel selection to get parent individuals.
                                            
 
                                            
 
                                            Returns:
                                            
@@ -149,25 +149,25 @@ 
                                            Submodules
-
                                            src.selection.selection_operator module
                                            
+
                                            
+
                                            pycellga.selection.selection_operator module
                                            
 
                                            
-
                                            
-class src.selection.selection_operator.SelectionOperator[source]
                                            
+
                                            
+class pycellga.selection.selection_operator.SelectionOperator[source]
                                            
 
                                            Bases: object
                                            
 
                                            
-
                                            
-get_parents() → list[source]
                                            
+
                                            
+get_parents() → list[source]
                                            
 
                                            
 
 
                                            
 
 
                                            
-
                                            
-
                                            src.selection.tournament_selection module
                                            
+
                                            
+
                                            pycellga.selection.tournament_selection module
                                            
 
                                            
-
                                            
-class src.selection.tournament_selection.TournamentSelection(pop_list: List[Individual] = [], c: int = 0, K: int = 2)[source]
                                            
+
                                            
+class pycellga.selection.tournament_selection.TournamentSelection(pop_list: List[Individual] = [], c: int = 0, K: int = 2)[source]
                                            
 
                                            Bases: SelectionOperator
                                            
 
                                            TournamentSelection performs a tournament selection on a population of individuals
 to select parent individuals for crossover.
                                            
@@ -181,8 +181,8 @@ 
                                            Submodules
-
                                            
-__init__(pop_list: List[Individual] = [], c: int = 0, K: int = 2)[source]
                                            
+
                                            
+__init__(pop_list: List[Individual] = [], c: int = 0, K: int = 2)[source]
                                            
 
                                            Initialize the TournamentSelection object.
                                            
 
                                            
 
                                            Parameters:
                                            
@@ -196,8 +196,8 @@ 
                                            Submodules
-
                                            
-get_parents() → List[Individual][source]
                                            
+
                                            
+get_parents() → List[Individual][source]
                                            
 
                                            Perform the tournament selection to get parent individuals.
                                            
 
                                            
 
                                            Returns:
                                            
@@ -212,8 +212,8 @@ 
                                            Submodules
-
                                            Module contents
                                            
+
                                            
+
                                            Module contents
                                            
 
                                            
 
                                            
 
@@ -221,8 +221,8 @@ 
                                            Submodules
-        
-        
+        
+        
     
                                            
 
   
                                            
diff --git a/pycellga.tests.html b/pycellga.tests.html
new file mode 100644
index 0000000..ff69d1d
--- /dev/null
+++ b/pycellga.tests.html
@@ -0,0 +1,2699 @@
+
+
+
+  
+
+  
+  pycellga.tests package — PYCELLGA Documentation 1.0.0 documentation
+      
+      
+
+  
+  
+  
+        
+        
+        
+        
+        
+    
+    
+    
+     
+
+
+ 
+  
                                            
+    
+
+    
                                            
+
+      
                                            
+        
                                            
+          
                                            
+  
+  
                                            
+
                                            
+          
                                            
+           
                                            
+             
+  
                                            
+
                                            pycellga.tests package
                                            
+
                                            
+
                                            Submodules
                                            
+
                                            
+
                                            
+
                                            pycellga.tests.conftest module
                                            
+
                                            
+
                                            
+
                                            pycellga.tests.test_ackley module
                                            
+
                                            
+
                                            
+pycellga.tests.test_ackley.test_ackley()[source]
                                            
+
                                            Test the Ackley function implementation.
                                            
+
                                            This test verifies the correctness of the Ackley function by evaluating it at several points and
+comparing the results to expected values.
                                            
+
                                            The Ackley function is commonly used as a benchmark for optimization algorithms. It is a continuous
+function with multiple local minima and a single global minimum.
                                            
+
                                            The test performs the following checks:
+1. Evaluates the Ackley function at a set of given points.
+2. Compares the computed values to expected results.
                                            
+
                                            
+
                                            Parameters:
                                            
+
                                            None
                                            
+
                                            
+
                                            Raises:
                                            
+
                                            AssertionError – If any of the computed values do not match the expected values.
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_arithmetic_crossover module
                                            
+
                                            
+
                                            
+class pycellga.tests.test_arithmetic_crossover.MockProblem[source]
                                            
+
                                            Bases: AbstractProblem
                                            
+
                                            A mock problem class for testing purposes.
                                            
+
                                            
+
                                            
+f(x: list) → float[source]
                                            
+
                                            A mock fitness function that simply sums the chromosome values.
                                            
+
                                            
+
                                            Parameters:
                                            
+
                                            x (list) – A list of float variables.
                                            
+
                                            
+
                                            Returns:
                                            
+
                                            The sum of the list values.
                                            
+
                                            
+
                                            Return type:
                                            
+
                                            float
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_arithmetic_crossover.setup_parents()[source]
                                            
+
                                            Fixture for creating a pair of parent Individual instances.
                                            
+
                                            
+
                                            Returns:
                                            
+
                                            A list containing two parent individuals with predefined chromosomes and size.
                                            
+
                                            
+
                                            Return type:
                                            
+
                                            list
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_arithmetic_crossover.setup_problem()[source]
                                            
+
                                            Fixture for creating a mock problem instance.
                                            
+
                                            
+
                                            Returns:
                                            
+
                                            An instance of the mock problem.
                                            
+
                                            
+
                                            Return type:
                                            
+
                                            MockProblem
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_arithmetic_crossover.test_arithmetic_crossover(setup_parents, setup_problem)[source]
                                            
+
                                            Test the ArithmeticCrossover function implementation.
                                            
+
                                            This test checks the arithmetic crossover on a pair of parent individuals by verifying the recombination
+operation and the integrity of the offspring chromosomes.
                                            
+
                                            
+
                                            Parameters:
                                            
+
                                              
+
                                            • setup_parents (fixture) – The fixture providing the parent individuals.
                                              • 
+
                                            • setup_problem (fixture) – The fixture providing the mock problem instance.
                                              • 
+
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_bentcigar_function module
                                            
+
                                            
+
                                            
+pycellga.tests.test_bentcigar_function.setup_bentcigar()[source]
                                            
+
                                            Fixture for creating an instance of the Bentcigar problem.
                                            
+
                                            
+
                                            Returns:
                                            
+
                                            An instance of the Bentcigar problem.
                                            
+
                                            
+
                                            Return type:
                                            
+
                                            Bentcigar
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_bentcigar_function.test_bentcigar_function(setup_bentcigar)[source]
                                            
+
                                            Test the Bentcigar function implementation.
                                            
+
                                            This test checks the calculation of the Bentcigar function value for given lists of float variables.
+It uses predefined inputs and compares the outputs to the expected values.
                                            
+
                                            
+
                                            Parameters:
                                            
+
                                            setup_bentcigar (fixture) – The fixture providing the Bentcigar problem instance.
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_bit_flip_mutation module
                                            
+
                                            
+
                                            
+pycellga.tests.test_bit_flip_mutation.test_bit_flip_mutation()[source]
                                            
+
                                            Test the BitFlipMutation class for the Individual class on the OneMax problem.
                                            
+
                                            This test verifies that the BitFlipMutation correctly mutates the chromosome of
+an Individual by flipping some bits and ensures that the mutation is applied
+correctly. The test checks that:
+1. The chromosome is mutated.
+2. The chromosome size remains unchanged.
+3. The fitness value of the mutated individual is computed correctly.
                                            
+
                                            The function uses a fixed random seed to ensure reproducibility of the results.
                                            
+
                                            Notes
                                            
+
                                            The test assumes that the BitFlipMutation class correctly implements bit flipping
+mutation and that the OneMax problem correctly evaluates the fitness of an individual.
                                            
+
                                            The following assertions are made:
+- At least one bit in the chromosome is changed.
+- The size of the mutated individual’s chromosome matches the original size.
+- The fitness value of the mutated individual is calculated correctly.
                                            
+
                                            
+
                                            Raises:
                                            
+
                                            AssertionError – If any of the conditions for correctness are not met.
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_blxalpha_crossover module
                                            
+
                                            
+
                                            
+class pycellga.tests.test_blxalpha_crossover.MockProblem[source]
                                            
+
                                            Bases: AbstractProblem
                                            
+
                                            A mock problem class for testing purposes.
                                            
+
                                            
+
                                            
+f(x: list) → float[source]
                                            
+
                                            A mock fitness function that simply sums the chromosome values.
                                            
+
                                            
+
                                            Parameters:
                                            
+
                                            x (list) – A list of float variables.
                                            
+
                                            
+
                                            Returns:
                                            
+
                                            The sum of the list values.
                                            
+
                                            
+
                                            Return type:
                                            
+
                                            float
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_blxalpha_crossover.setup_parents()[source]
                                            
+
                                            Fixture for creating a pair of parent Individual instances.
                                            
+
                                            
+
                                            Returns:
                                            
+
                                            A list containing two parent individuals with predefined chromosomes and size.
                                            
+
                                            
+
                                            Return type:
                                            
+
                                            list
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_blxalpha_crossover.setup_problem()[source]
                                            
+
                                            Fixture for creating a mock problem instance.
                                            
+
                                            
+
                                            Returns:
                                            
+
                                            An instance of the mock problem.
                                            
+
                                            
+
                                            Return type:
                                            
+
                                            MockProblem
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_blxalpha_crossover.test_blxalpha_crossover(setup_parents, setup_problem)[source]
                                            
+
                                            Test the BlxalphaCrossover function implementation.
                                            
+
                                            This test checks the BLX-alpha crossover on a pair of parent individuals by verifying the recombination
+operation and the integrity of the offspring chromosomes.
                                            
+
                                            
+
                                            Parameters:
                                            
+
                                              
+
                                            • setup_parents (fixture) – The fixture providing the parent individuals.
                                              • 
+
                                            • setup_problem (fixture) – The fixture providing the mock problem instance.
                                              • 
+
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_bohachevsky module
                                            
+
                                            
+
                                            
+pycellga.tests.test_bohachevsky.test_bohachevsky()[source]
                                            
+
                                            Test the Bohachevsky function implementation.
                                            
+
                                            This test verifies the correctness of the Bohachevsky function by evaluating it at several points and
+comparing the results to expected values.
                                            
+
                                            The Bohachevsky function is used as a benchmark for optimization algorithms. It has multiple local minima
+and is designed to test the performance of optimization algorithms.
                                            
+
                                            The test performs the following checks:
+1. Evaluates the Bohachevsky function at specific points.
+2. Compares the computed values to the expected rounded results.
                                            
+
                                            
+
                                            Parameters:
                                            
+
                                            None
                                            
+
                                            
+
                                            Raises:
                                            
+
                                            AssertionError – If any of the computed values do not match the expected rounded values.
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_byte_mutation module
                                            
+
                                            
+
                                            
+class pycellga.tests.test_byte_mutation.MockProblem[source]
                                            
+
                                            Bases: AbstractProblem
                                            
+
                                            A mock problem class for testing purposes.
                                            
+
                                            
+
                                            
+f(x: list) → float[source]
                                            
+
                                            A mock fitness function that simply sums the chromosome values.
                                            
+
                                            
+
                                            Parameters:
                                            
+
                                            x (list) – A list of float variables.
                                            
+
                                            
+
                                            Returns:
                                            
+
                                            The sum of the list values.
                                            
+
                                            
+
                                            Return type:
                                            
+
                                            float
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_byte_mutation.setup_individual()[source]
                                            
+
                                            Fixture for creating a sample Individual instance.
                                            
+
                                            
+
                                            Returns:
                                            
+
                                            An individual instance with a predefined chromosome and size.
                                            
+
                                            
+
                                            Return type:
                                            
+
                                            Individual
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_byte_mutation.setup_problem()[source]
                                            
+
                                            Fixture for creating a mock problem instance.
                                            
+
                                            
+
                                            Returns:
                                            
+
                                            An instance of the mock problem.
                                            
+
                                            
+
                                            Return type:
                                            
+
                                            MockProblem
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_byte_mutation.test_byte_mutation(setup_individual, setup_problem)[source]
                                            
+
                                            Test the ByteMutation function implementation.
                                            
+
                                            This test checks the byte-wise mutation on an individual’s chromosome by verifying the mutation
+operation and the integrity of the chromosome.
                                            
+
                                            
+
                                            Parameters:
                                            
+
                                              
+
                                            • setup_individual (fixture) – The fixture providing the sample individual.
                                              • 
+
                                            • setup_problem (fixture) – The fixture providing the mock problem instance.
                                              • 
+
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_byte_mutation_random module
                                            
+
                                            
+
                                            
+class pycellga.tests.test_byte_mutation_random.MockProblem[source]
                                            
+
                                            Bases: AbstractProblem
                                            
+
                                            A mock problem class for testing purposes.
                                            
+
                                            
+
                                            
+f(x: list) → float[source]
                                            
+
                                            A mock fitness function that simply sums the chromosome values.
                                            
+
                                            
+
                                            Parameters:
                                            
+
                                            x (list) – A list of float variables.
                                            
+
                                            
+
                                            Returns:
                                            
+
                                            The sum of the list values.
                                            
+
                                            
+
                                            Return type:
                                            
+
                                            float
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_byte_mutation_random.setup_individual()[source]
                                            
+
                                            Fixture for creating a sample Individual instance.
                                            
+
                                            
+
                                            Returns:
                                            
+
                                            An individual instance with a predefined chromosome and size.
                                            
+
                                            
+
                                            Return type:
                                            
+
                                            Individual
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_byte_mutation_random.setup_problem()[source]
                                            
+
                                            Fixture for creating a mock problem instance.
                                            
+
                                            
+
                                            Returns:
                                            
+
                                            An instance of the mock problem.
                                            
+
                                            
+
                                            Return type:
                                            
+
                                            MockProblem
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_byte_mutation_random.test_byte_mutation_random(setup_individual, setup_problem)[source]
                                            
+
                                            Test the ByteMutationRandom function implementation.
                                            
+
                                            This test checks the byte mutation on an individual’s chromosome by verifying the mutation
+operation and the integrity of the chromosome.
                                            
+
                                            
+
                                            Parameters:
                                            
+
                                              
+
                                            • setup_individual (fixture) – The fixture providing the sample individual.
                                              • 
+
                                            • setup_problem (fixture) – The fixture providing the mock problem instance.
                                              • 
+
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_byte_one_point_crossover module
                                            
+
                                            
+
                                            
+class pycellga.tests.test_byte_one_point_crossover.MockProblem[source]
                                            
+
                                            Bases: AbstractProblem
                                            
+
                                            A mock problem class for testing purposes.
                                            
+
                                            
+
                                            
+f(x: list) → float[source]
                                            
+
                                            A mock fitness function that simply sums the chromosome values.
                                            
+
                                            
+
                                            Parameters:
                                            
+
                                            x (list) – A list of float variables.
                                            
+
                                            
+
                                            Returns:
                                            
+
                                            The sum of the list values.
                                            
+
                                            
+
                                            Return type:
                                            
+
                                            float
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_byte_one_point_crossover.setup_parents()[source]
                                            
+
                                            Fixture for creating a pair of parent Individual instances.
                                            
+
                                            
+
                                            Returns:
                                            
+
                                            A list containing two parent individuals with predefined chromosomes and size.
                                            
+
                                            
+
                                            Return type:
                                            
+
                                            list
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_byte_one_point_crossover.setup_problem()[source]
                                            
+
                                            Fixture for creating a mock problem instance.
                                            
+
                                            
+
                                            Returns:
                                            
+
                                            An instance of the mock problem.
                                            
+
                                            
+
                                            Return type:
                                            
+
                                            MockProblem
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_byte_one_point_crossover.test_byte_one_point_crossover(setup_parents, setup_problem)[source]
                                            
+
                                            Test the ByteOnePointCrossover function implementation.
                                            
+
                                            This test checks the byte-level one-point crossover on a pair of parent individuals by verifying the recombination
+operation and the integrity of the offspring chromosomes.
                                            
+
                                            
+
                                            Parameters:
                                            
+
                                              
+
                                            • setup_parents (fixture) – The fixture providing the parent individuals.
                                              • 
+
                                            • setup_problem (fixture) – The fixture providing the mock problem instance.
                                              • 
+
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_byte_operators module
                                            
+
                                            
+
                                            
+pycellga.tests.test_byte_operators.test_bits_to_float()[source]
                                            
+
                                            Test the bits_to_float function.
                                            
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_byte_operators.test_bits_to_floats()[source]
                                            
+
                                            Test the bits_to_floats function.
                                            
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_byte_operators.test_float_to_bits()[source]
                                            
+
                                            Test the float_to_bits function.
                                            
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_byte_operators.test_floats_to_bits()[source]
                                            
+
                                            Test the floats_to_bits function.
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_byte_uniform_crossover module
                                            
+
                                            
+
                                            
+class pycellga.tests.test_byte_uniform_crossover.MockProblem[source]
                                            
+
                                            Bases: AbstractProblem
                                            
+
                                            A mock problem class for testing purposes.
                                            
+
                                            
+
                                            
+f(x: list) → float[source]
                                            
+
                                            A mock fitness function that simply sums the chromosome values.
                                            
+
                                            
+
                                            Parameters:
                                            
+
                                            x (list) – A list of float variables.
                                            
+
                                            
+
                                            Returns:
                                            
+
                                            The sum of the list values.
                                            
+
                                            
+
                                            Return type:
                                            
+
                                            float
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_byte_uniform_crossover.setup_parents()[source]
                                            
+
                                            Fixture for creating a pair of parent Individual instances.
                                            
+
                                            
+
                                            Returns:
                                            
+
                                            A list containing two parent individuals with predefined chromosomes and size.
                                            
+
                                            
+
                                            Return type:
                                            
+
                                            list
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_byte_uniform_crossover.setup_problem()[source]
                                            
+
                                            Fixture for creating a mock problem instance.
                                            
+
                                            
+
                                            Returns:
                                            
+
                                            An instance of the mock problem.
                                            
+
                                            
+
                                            Return type:
                                            
+
                                            MockProblem
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_byte_uniform_crossover.test_byte_uniform_crossover(setup_parents, setup_problem)[source]
                                            
+
                                            Test the ByteUniformCrossover function implementation.
                                            
+
                                            This test checks the byte-level uniform crossover on a pair of parent individuals by verifying the recombination
+operation and the integrity of the offspring chromosomes.
                                            
+
                                            
+
                                            Parameters:
                                            
+
                                              
+
                                            • setup_parents (fixture) – The fixture providing the parent individuals.
                                              • 
+
                                            • setup_problem (fixture) – The fixture providing the mock problem instance.
                                              • 
+
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_chichinadze_function module
                                            
+
                                            
+
                                            
+pycellga.tests.test_chichinadze_function.setup_chichinadze()[source]
                                            
+
                                            Fixture for creating an instance of the Chichinadze problem.
                                            
+
                                            
+
                                            Returns:
                                            
+
                                            An instance of the Chichinadze problem.
                                            
+
                                            
+
                                            Return type:
                                            
+
                                            Chichinadze
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_chichinadze_function.test_chichinadze_function(setup_chichinadze)[source]
                                            
+
                                            Test the Chichinadze function implementation.
                                            
+
                                            This test checks the calculation of the Chichinadze function value for given lists of float variables.
+It uses predefined inputs and compares the outputs to the expected values.
                                            
+
                                            
+
                                            Parameters:
                                            
+
                                            setup_chichinadze (fixture) – The fixture providing the Chichinadze problem instance.
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_compact_13 module
                                            
+
                                            
+
                                            
+pycellga.tests.test_compact_13.test_compact_13()[source]
                                            
+
                                            Test the Compact13 class for calculating neighbor positions in a grid.
                                            
+
                                            This test verifies that the Compact13 class correctly calculates the positions
+of the 12 neighbors surrounding a given position in a grid. It ensures that:
+1. The number of neighbors is always 12 for positions not on the boundary of the grid.
+2. Positions on the boundary of the grid still correctly return 12 neighbors.
                                            
+
                                            The test uses three different positions within the grid to validate the functionality
+of the Compact13 neighborhood calculation.
                                            
+
                                            Notes
                                            
+
                                            The Compact13 neighborhood considers all 12 surrounding cells in a 5x5 grid centered
+on a given position, excluding the cell itself. The grid is assumed to be toroidal,
+meaning that positions on the edges wrap around to the opposite edge.
                                            
+
                                            The following assertions are made:
+- The number of calculated neighbor positions is 12 for various positions in the grid.
                                            
+
                                            
+
                                            Raises:
                                            
+
                                            AssertionError – If the number of calculated neighbor positions is not 12.
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_compact_21 module
                                            
+
                                            
+
                                            
+pycellga.tests.test_compact_21.test_compact_21()[source]
                                            
+
                                            Test the Compact21 class for calculating neighbor positions in a grid.
                                            
+
                                            This test verifies that the Compact21 class correctly calculates the positions
+of the 20 neighbors surrounding a given position in a grid. It ensures that:
+1. The number of neighbors is always 20 for positions not on the boundary of the grid.
+2. Positions on the boundary of the grid still correctly return 20 neighbors.
                                            
+
                                            The test uses three different positions within the grid to validate the functionality
+of the Compact21 neighborhood calculation.
                                            
+
                                            Notes
                                            
+
                                            The Compact21 neighborhood considers all 20 surrounding cells in a 5x5 grid centered
+on a given position, excluding the cell itself. The grid is assumed to be toroidal,
+meaning that positions on the edges wrap around to the opposite edge.
                                            
+
                                            The following assertions are made:
+- The number of calculated neighbor positions is 20 for various positions in the grid.
                                            
+
                                            
+
                                            Raises:
                                            
+
                                            AssertionError – If the number of calculated neighbor positions is not 20.
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_compact_25 module
                                            
+
                                            
+
                                            
+pycellga.tests.test_compact_25.test_compact_25()[source]
                                            
+
                                            Test the Compact25 class for calculating neighbor positions in a grid.
                                            
+
                                            This test verifies that the Compact25 class correctly calculates the positions
+of the 24 neighbors surrounding a given position in a grid. It ensures that:
+1. The number of neighbors is always 24 for positions not on the boundary of the grid.
+2. Positions on the boundary of the grid still correctly return 24 neighbors.
                                            
+
                                            The test uses three different positions within the grid to validate the functionality
+of the Compact25 neighborhood calculation.
                                            
+
                                            Notes
                                            
+
                                            The Compact25 neighborhood considers all 24 surrounding cells in a 5x5 grid centered
+on a given position, excluding the cell itself. The grid is assumed to be toroidal,
+meaning that positions on the edges wrap around to the opposite edge.
                                            
+
                                            The following assertions are made:
+- The number of calculated neighbor positions is 24 for various positions in the grid.
                                            
+
                                            
+
                                            Raises:
                                            
+
                                            AssertionError – If the number of calculated neighbor positions is not 24.
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_compact_9 module
                                            
+
                                            
+
                                            
+pycellga.tests.test_compact_9.test_compact_9()[source]
                                            
+
                                            Test the Compact9 class for calculating neighbor positions in a grid.
                                            
+
                                            This test verifies that the Compact9 class correctly calculates the positions
+of the 8 neighbors surrounding a given position in a grid. It ensures that:
+1. The number of neighbors is always 8 for positions not on the boundary of the grid.
+2. Positions on the boundary of the grid still correctly return 8 neighbors.
                                            
+
                                            The test uses three different positions within the grid to validate the functionality
+of the Compact9 neighborhood calculation.
                                            
+
                                            Notes
                                            
+
                                            The Compact9 neighborhood considers all 8 surrounding cells in a 3x3 grid centered
+on a given position. The grid is assumed to be toroidal, meaning that positions on the
+edges wrap around to the opposite edge.
                                            
+
                                            The following assertions are made:
+- The number of calculated neighbor positions is 8 for various positions in the grid.
                                            
+
                                            
+
                                            Raises:
                                            
+
                                            AssertionError – If the number of calculated neighbor positions is not 8.
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_count_sat module
                                            
+
                                            
+
                                            
+pycellga.tests.test_count_sat.test_count_sat()[source]
                                            
+
                                            Test the CountSat function implementation.
                                            
+
                                            This test verifies the calculation of the CountSat function value for specific binary input values.
                                            
+
                                            The CountSat function evaluates the satisfaction of a set of binary clauses. This test ensures the
+function computes the correct results for specific test inputs, including cases where all variables
+are set to 1.
                                            
+
                                            Examples
                                            
+
                                            >>> test_count_sat()
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_dropwave_function module
                                            
+
                                            
+
                                            
+pycellga.tests.test_dropwave_function.setup_dropwave()[source]
                                            
+
                                            Fixture for creating an instance of the Dropwave problem.
                                            
+
                                            
+
                                            Returns:
                                            
+
                                            An instance of the Dropwave problem.
                                            
+
                                            
+
                                            Return type:
                                            
+
                                            Dropwave
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_dropwave_function.test_dropwave_function(setup_dropwave)[source]
                                            
+
                                            Test the Dropwave function implementation.
                                            
+
                                            This test checks the calculation of the Dropwave function value for given lists of float variables.
+It uses predefined inputs and compares the outputs to the expected values.
                                            
+
                                            
+
                                            Parameters:
                                            
+
                                            setup_dropwave (fixture) – The fixture providing the Dropwave problem instance.
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_ecc module
                                            
+
                                            
+
                                            
+pycellga.tests.test_ecc.ecc_instance()[source]
                                            
+
                                            Fixture for creating an instance of the Ecc class.
                                            
+
                                            This fixture returns an instance of the Ecc class to be used in tests.
                                            
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_ecc.test_ecc(ecc_instance)[source]
                                            
+
                                            Test the ECC function implementation.
                                            
+
                                            This test checks the calculation of the ECC function value for a given list of binary variables.
+It uses predefined inputs and compares the outputs to expected values.
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_flat_crossover module
                                            
+
                                            
+
                                            
+class pycellga.tests.test_flat_crossover.MockProblem[source]
                                            
+
                                            Bases: AbstractProblem
                                            
+
                                            A mock problem class for testing purposes.
                                            
+
                                            
+
                                            
+f(x: list) → float[source]
                                            
+
                                            A mock fitness function that simply sums the chromosome values.
                                            
+
                                            
+
                                            Parameters:
                                            
+
                                            x (list) – A list of float variables.
                                            
+
                                            
+
                                            Returns:
                                            
+
                                            The sum of the list values.
                                            
+
                                            
+
                                            Return type:
                                            
+
                                            float
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_flat_crossover.setup_parents()[source]
                                            
+
                                            Fixture for creating a pair of parent Individual instances.
                                            
+
                                            
+
                                            Returns:
                                            
+
                                            A list containing two parent individuals with predefined chromosomes and size.
                                            
+
                                            
+
                                            Return type:
                                            
+
                                            list
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_flat_crossover.setup_problem()[source]
                                            
+
                                            Fixture for creating a mock problem instance.
                                            
+
                                            
+
                                            Returns:
                                            
+
                                            An instance of the mock problem.
                                            
+
                                            
+
                                            Return type:
                                            
+
                                            MockProblem
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_flat_crossover.test_flat_crossover(setup_parents, setup_problem)[source]
                                            
+
                                            Test the FlatCrossover function implementation.
                                            
+
                                            This test checks the flat crossover on a pair of parent individuals by verifying the recombination
+operation and the integrity of the offspring chromosomes.
                                            
+
                                            
+
                                            Parameters:
                                            
+
                                              
+
                                            • setup_parents (fixture) – The fixture providing the parent individuals.
                                              • 
+
                                            • setup_problem (fixture) – The fixture providing the mock problem instance.
                                              • 
+
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_float_uniform_mutation module
                                            
+
                                            
+
                                            
+class pycellga.tests.test_float_uniform_mutation.MockProblem[source]
                                            
+
                                            Bases: AbstractProblem
                                            
+
                                            A mock problem class for testing purposes.
                                            
+
                                            
+
                                            
+f(x: list) → float[source]
                                            
+
                                            A mock fitness function that simply sums the chromosome values.
                                            
+
                                            
+
                                            Parameters:
                                            
+
                                            x (list) – A list of float variables.
                                            
+
                                            
+
                                            Returns:
                                            
+
                                            The sum of the list values.
                                            
+
                                            
+
                                            Return type:
                                            
+
                                            float
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_float_uniform_mutation.setup_individual()[source]
                                            
+
                                            Fixture for creating a sample Individual instance.
                                            
+
                                            
+
                                            Returns:
                                            
+
                                            An individual instance with a predefined chromosome and size.
                                            
+
                                            
+
                                            Return type:
                                            
+
                                            Individual
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_float_uniform_mutation.setup_problem()[source]
                                            
+
                                            Fixture for creating a mock problem instance.
                                            
+
                                            
+
                                            Returns:
                                            
+
                                            An instance of the mock problem.
                                            
+
                                            
+
                                            Return type:
                                            
+
                                            MockProblem
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_float_uniform_mutation.test_float_uniform_mutation(setup_individual, setup_problem)[source]
                                            
+
                                            Test the FloatUniformMutation function implementation.
                                            
+
                                            This test checks the uniform mutation on an individual’s chromosome by verifying the mutation
+operation and the integrity of the chromosome.
                                            
+
                                            
+
                                            Parameters:
                                            
+
                                              
+
                                            • setup_individual (fixture) – The fixture providing the sample individual.
                                              • 
+
                                            • setup_problem (fixture) – The fixture providing the mock problem instance.
                                              • 
+
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_fms module
                                            
+
                                            
+
                                            
+pycellga.tests.test_fms.fms_instance()[source]
                                            
+
                                            Fixture for creating an instance of the Fms class.
                                            
+
                                            This fixture returns an instance of the Fms class to be used in tests.
                                            
+
                                            
+
                                            Returns:
                                            
+
                                            An instance of the Fms class.
                                            
+
                                            
+
                                            Return type:
                                            
+
                                            Fms
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_fms.test_fms(fms_instance)[source]
                                            
+
                                            Test the Fms function implementation.
                                            
+
                                            This test checks the calculation of the FMS function value for a given list of binary variables.
+It uses a predefined input and compares the output to the expected value.
                                            
+
                                            
+
                                            Parameters:
                                            
+
                                            fms_instance (Fms) – An instance of the Fms class.
                                            
+
                                            
+
                                            
+
                                            Notes
                                            
+
                                            The test uses a randomly generated sample input chromosome of length 192 and checks if the function output is a float
+and non-negative. Additional checks with known values can be added for more thorough testing.
                                            
+
                                            
+
                                            Assertions
                                            
+
                                              
+
                                            • The fitness value should be a float.
                                              • 
+
                                            • The fitness value should be non-negative, as it’s a sum of squares of differences.
                                              • 
+
                                            
+
                                            Examples
                                            
+
                                            >>> test_fms(fms_instance)
+
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_grid module
                                            
+
                                            
+
                                            
+pycellga.tests.test_grid.test_grid()[source]
                                            
+
                                            Test the Grid class implementation.
                                            
+
                                            This test checks the functionality of the Grid class for creating a 2D grid.
+It verifies the type, length, and content of the generated grid.
                                            
+
                                            The test performs the following steps:
+1. Create an instance of the Grid class with a 5x5 grid.
+2. Generate the 2D grid using the make_2d_grid method.
+3. Verify that the result is a list.
+4. Verify that the length of the list is 25 (5x5).
+5. Verify that each element in the list is a tuple.
+6. Verify the first and last elements of the list.
                                            
+
                                            
+
                                            Raises:
                                            
+
                                            AssertionError – If the result does not match the expected values.
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_griewank_function module
                                            
+
                                            
+
                                            
+pycellga.tests.test_griewank_function.setup_griewank()[source]
                                            
+
                                            Fixture for creating an instance of the Griewank problem.
                                            
+
                                            
+
                                            Returns:
                                            
+
                                            An instance of the Griewank problem.
                                            
+
                                            
+
                                            Return type:
                                            
+
                                            Griewank
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_griewank_function.test_griewank_function(setup_griewank)[source]
                                            
+
                                            Test the Griewank function implementation.
                                            
+
                                            This test checks the calculation of the Griewank function value for given lists of float variables.
+It uses predefined inputs and compares the outputs to the expected values.
                                            
+
                                            
+
                                            Parameters:
                                            
+
                                            setup_griewank (fixture) – The fixture providing the Griewank problem instance.
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_holzman_function module
                                            
+
                                            
+
                                            
+pycellga.tests.test_holzman_function.setup_holzman()[source]
                                            
+
                                            Fixture for creating an instance of the Holzman problem.
                                            
+
                                            
+
                                            Returns:
                                            
+
                                            An instance of the Holzman problem.
                                            
+
                                            
+
                                            Return type:
                                            
+
                                            Holzman
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_holzman_function.test_holzman_function(setup_holzman)[source]
                                            
+
                                            Test the Holzman function implementation.
                                            
+
                                            This test checks the calculation of the Holzman function value for given lists of float variables.
+It uses predefined inputs and compares the outputs to the expected values.
                                            
+
                                            
+
                                            Parameters:
                                            
+
                                            setup_holzman (fixture) – The fixture providing the Holzman problem instance.
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_individual module
                                            
+
                                            
+
                                            
+pycellga.tests.test_individual.setup_individual()[source]
                                            
+
                                            Fixture to provide an instance of the Individual class with different configurations.
                                            
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_individual.test_get_set_neighbors()[source]
                                            
+
                                            Test getting and setting the list of neighbors for the individual.
                                            
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_individual.test_get_set_neighbors_positions()[source]
                                            
+
                                            Test getting and setting the positions of the individual’s neighbors.
                                            
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_individual.test_illegal_genome_type()[source]
                                            
+
                                            Test that an exception is raised when an illegal genome type is provided.
                                            
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_individual.test_individual_init()[source]
                                            
+
                                            Test the initialization of the Individual class.
                                            
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_individual.test_randomize_binary()[source]
                                            
+
                                            Test the randomization of the chromosome for a binary genome type.
                                            
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_individual.test_randomize_permutation()[source]
                                            
+
                                            Test the randomization of the chromosome for a permutation genome type.
                                            
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_individual.test_randomize_real_valued()[source]
                                            
+
                                            Test the randomization of the chromosome for a real-valued genome type.
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_insertion_mutation module
                                            
+
                                            
+
                                            
+pycellga.tests.test_insertion_mutation.test_insertion_mutation()[source]
                                            
+
                                            Test the InsertionMutation class for the Individual class on the TSP problem.
                                            
+
                                            This test verifies that the InsertionMutation correctly mutates the chromosome of
+an Individual by inserting elements and ensures that the mutation is applied
+correctly. The test checks that:
+1. The chromosome is mutated.
+2. The chromosome size remains unchanged.
                                            
+
                                            The function uses random permutation for the chromosome initialization.
                                            
+
                                            Notes
                                            
+
                                            The test assumes that the InsertionMutation class correctly implements insertion
+mutation and that the TSP problem correctly evaluates the fitness of an individual.
                                            
+
                                            The following assertions are made:
+- At least one element in the chromosome is changed.
+- The size of the mutated individual’s chromosome matches the original size.
                                            
+
                                            
+
                                            Raises:
                                            
+
                                            AssertionError – If any of the conditions for correctness are not met.
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_levy_function module
                                            
+
                                            
+
                                            
+pycellga.tests.test_levy_function.setup_levy()[source]
                                            
+
                                            Fixture to provide an instance of the Levy problem.
                                            
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_levy_function.test_levy_function(setup_levy)[source]
                                            
+
                                            Test the Levy function implementation.
                                            
+
                                            This test checks the calculation of the Levy function value for given lists of float variables.
+It uses predefined inputs and compares the outputs to the expected values.
                                            
+
                                            
+
                                            Parameters:
                                            
+
                                            setup_levy (fixture) – The fixture providing the Levy problem instance.
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_linear_5 module
                                            
+
                                            
+
                                            
+pycellga.tests.test_linear_5.test_linear_5()[source]
                                            
+
                                            Test the Linear5 class for calculating neighbor positions in a grid.
                                            
+
                                            This test verifies that the Linear5 class correctly calculates the positions
+of the 4 neighbors surrounding a given position in a grid. It ensures that:
+1. The number of neighbors is always 4, which should include the positions
                                            
+
                                            
+
                                            directly adjacent in the linear neighborhood.
                                            
+
                                            
+
                                            The test uses three different positions within the grid to validate the functionality
+of the Linear5 neighborhood calculation.
                                            
+
                                            Notes
                                            
+
                                            The Linear5 neighborhood considers 4 neighboring cells aligned in a linear fashion
+(left, right, up, and down) in a 5x5 grid centered on a given position.
                                            
+
                                            The following assertions are made:
+- The number of calculated neighbor positions is 4 for various positions in the grid.
                                            
+
                                            
+
                                            Raises:
                                            
+
                                            AssertionError – If the number of calculated neighbor positions is not 4.
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_linear_9 module
                                            
+
                                            
+
                                            
+pycellga.tests.test_linear_9.test_linear_9()[source]
                                            
+
                                            Test the Linear9 class for calculating neighbor positions in a grid.
                                            
+
                                            This test verifies that the Linear9 class correctly calculates the positions
+of the 8 neighbors surrounding a given position in a grid. It ensures that:
+1. The number of neighbors is always 8, which should include the positions
                                            
+
                                            
+
                                            surrounding the given position in the linear neighborhood.
                                            
+
                                            
+
                                            The test uses three different positions within the grid to validate the functionality
+of the Linear9 neighborhood calculation.
                                            
+
                                            Notes
                                            
+
                                            The Linear9 neighborhood considers 8 neighboring cells aligned in a linear fashion
+(left, right, up, down, and the diagonals) in a 5x5 grid centered on a given position.
                                            
+
                                            The following assertions are made:
+- The number of calculated neighbor positions is 8 for various positions in the grid.
                                            
+
                                            
+
                                            Raises:
                                            
+
                                            AssertionError – If the number of calculated neighbor positions is not 8.
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_linear_crossover module
                                            
+
                                            
+
                                            
+class pycellga.tests.test_linear_crossover.MockProblem[source]
                                            
+
                                            Bases: AbstractProblem
                                            
+
                                            A mock problem class for testing purposes.
                                            
+
                                            
+
                                            
+f(x: list) → float[source]
                                            
+
                                            A mock fitness function that simply sums the chromosome values.
                                            
+
                                            
+
                                            Parameters:
                                            
+
                                            x (list) – A list of float variables.
                                            
+
                                            
+
                                            Returns:
                                            
+
                                            The sum of the list values.
                                            
+
                                            
+
                                            Return type:
                                            
+
                                            float
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_linear_crossover.setup_parents()[source]
                                            
+
                                            Fixture for creating a pair of parent Individual instances.
                                            
+
                                            
+
                                            Returns:
                                            
+
                                            A list containing two parent individuals with predefined chromosomes and size.
                                            
+
                                            
+
                                            Return type:
                                            
+
                                            list
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_linear_crossover.setup_problem()[source]
                                            
+
                                            Fixture for creating a mock problem instance.
                                            
+
                                            
+
                                            Returns:
                                            
+
                                            An instance of the mock problem.
                                            
+
                                            
+
                                            Return type:
                                            
+
                                            MockProblem
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_linear_crossover.test_linear_crossover(setup_parents, setup_problem)[source]
                                            
+
                                            Test the LinearCrossover function implementation.
                                            
+
                                            This test checks the linear crossover on a pair of parent individuals by verifying the recombination
+operation and the integrity of the offspring chromosomes.
                                            
+
                                            
+
                                            Parameters:
                                            
+
                                              
+
                                            • setup_parents (fixture) – The fixture providing the parent individuals.
                                              • 
+
                                            • setup_problem (fixture) – The fixture providing the mock problem instance.
                                              • 
+
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_matyas_function module
                                            
+
                                            
+
                                            
+pycellga.tests.test_matyas_function.setup_matyas()[source]
                                            
+
                                            Fixture to provide an instance of the Matyas problem.
                                            
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_matyas_function.test_matyas_function(setup_matyas)[source]
                                            
+
                                            Test the Matyas function implementation.
                                            
+
                                            This test checks the calculation of the Matyas function value for given lists of float variables.
+It uses predefined inputs and compares the outputs to the expected values.
                                            
+
                                            
+
                                            Parameters:
                                            
+
                                            setup_matyas (fixture) – The fixture providing the Matyas problem instance.
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_maxcut100 module
                                            
+
                                            
+
                                            
+pycellga.tests.test_maxcut100.maxcut_instance()[source]
                                            
+
                                            Fixture for creating an instance of the Maxcut100 class.
                                            
+
                                            This fixture returns an instance of the Maxcut100 class to be used in tests.
                                            
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_maxcut100.test_maxcut100()[source]
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_maxcut20_01 module
                                            
+
                                            
+
                                            
+pycellga.tests.test_maxcut20_01.maxcut_instance()[source]
                                            
+
                                            Fixture for creating an instance of the Maxcut20_01 class.
                                            
+
                                            This fixture returns an instance of the Maxcut20_01 class to be used in tests.
                                            
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_maxcut20_01.test_maxcut20_01(maxcut_instance)[source]
                                            
+
                                            Test the MAXCUT function implementation.
                                            
+
                                            This test checks the calculation of the MAXCUT function value for a given list of binary variables.
+It uses predefined inputs and compares the outputs to expected values.
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_maxcut20_09 module
                                            
+
                                            
+
                                            
+pycellga.tests.test_maxcut20_09.maxcut_instance()[source]
                                            
+
                                            Fixture for creating an instance of the Maxcut20_09 class.
                                            
+
                                            This fixture returns an instance of the Maxcut20_09 class to be used in tests.
                                            
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_maxcut20_09.test_maxcut20_09(maxcut_instance)[source]
                                            
+
                                            Test the MAXCUT function implementation.
                                            
+
                                            This test checks the calculation of the MAXCUT function value for a given list of binary variables.
+It uses predefined inputs and compares the outputs to expected values.
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_mmdp module
                                            
+
                                            
+
                                            
+pycellga.tests.test_mmdp.mmdp_instance()[source]
                                            
+
                                            Fixture for creating an instance of the Mmdp class.
                                            
+
                                            This fixture returns an instance of the Mmdp class to be used in tests.
                                            
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_mmdp.test_mmdp_function(mmdp_instance)[source]
                                            
+
                                            Test the Mmdp function implementation.
                                            
+
                                            This test checks the calculation of the MMDP fitness value for given lists of binary variables.
+It uses predefined inputs and compares the outputs to the expected values.
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_one_max module
                                            
+
                                            
+
                                            
+pycellga.tests.test_one_max.test_one_max()[source]
                                            
+
                                            Test the OneMax function implementation.
                                            
+
                                            This test verifies the calculation of the OneMax function value for specific binary input values.
                                            
+
                                            The OneMax function evaluates the number of 1s in a binary list. This test ensures that the function
+computes the correct number of 1s for various test inputs.
                                            
+
                                            Examples
                                            
+
                                            >>> test_one_max()
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_one_point_crossover module
                                            
+
                                            
+
                                            
+pycellga.tests.test_one_point_crossover.test_one_point_crossover()[source]
                                            
+
                                            Test the OnePointCrossover class for generating offspring from two parents.
                                            
+
                                            This test verifies that the OnePointCrossover class correctly performs one-point crossover
+between two parent individuals. It ensures that:
+1. The offspring generated have the same chromosome size as the parents.
+2. The chromosomes of the offspring contain only valid binary values (0 or 1).
                                            
+
                                            The test performs the following checks:
+- Both offspring have the same chromosome size.
+- Each chromosome in the offspring is composed of binary values (0 or 1).
                                            
+
                                            
+
                                            Raises:
                                            
+
                                            AssertionError – If any of the conditions for the crossover operation are not met.
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_optimizer_alpha_cga module
                                            
+
                                            
+
                                            
+class pycellga.tests.test_optimizer_alpha_cga.BinaryProblem[source]
                                            
+
                                            Bases: object
                                            
+
                                            Example problem class to be maximized for binary chromosomes.
                                            
+
                                            This class implements the OneMax problem where the goal is to maximize the number of 1s in a binary string.
                                            
+
                                            
+
                                            
+__init__()[source]
                                            
+
                                            
+
+
                                            
+
                                            
+f(x)[source]
                                            
+
                                            Compute the objective function value.
                                            
+
                                            This method counts the number of 1s in the binary chromosome.
                                            
+
                                            
+
                                            Parameters:
                                            
+
                                            x (list or numpy.ndarray) – The input chromosome represented as a list or array of binary values (0s and 1s).
                                            
+
                                            
+
                                            Returns:
                                            
+
                                            The computed value, which is the count of 1s in the chromosome.
                                            
+
                                            
+
                                            Return type:
                                            
+
                                            int
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
+
                                            
+
                                            
+class pycellga.tests.test_optimizer_alpha_cga.PermutationProblem(target: List[int])[source]
                                            
+
                                            Bases: object
                                            
+
                                            Example problem class to be minimized using a permutation-based approach.
                                            
+
                                            This class implements a simple objective function that measures the sum of absolute differences
+between the chromosome and a target permutation.
                                            
+
                                            
+
                                            
+__init__(target: List[int])[source]
                                            
+
                                            Initialize the PermutationProblem with a target permutation.
                                            
+
                                            
+
                                            Parameters:
                                            
+
                                            target (list of int) – The target permutation that the algorithm aims to find.
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+f(x: List[int]) → float[source]
                                            
+
                                            Compute the objective function value.
                                            
+
                                            This method implements the sum of absolute differences function.
                                            
+
                                            
+
                                            Parameters:
                                            
+
                                            x (list) – The input chromosome represented as a list of integers (permutation).
                                            
+
                                            
+
                                            Returns:
                                            
+
                                            The computed value of the function given the input x.
                                            
+
                                            
+
                                            Return type:
                                            
+
                                            float
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+test_optimizer_alpha_cga_permutation()[source]
                                            
+
                                            Test alpha_cga on a permutation-based problem where the target is the identity permutation.
                                            
+
                                            
+
+
                                            
+
+
                                            
+
                                            
+class pycellga.tests.test_optimizer_alpha_cga.RealProblem[source]
                                            
+
                                            Bases: object
                                            
+
                                            Example problem class to be minimized.
                                            
+
                                            This class implements a simple sum of squares function with a global minimum value of 0,
+achieved when all elements of the chromosome are equal to 0.
                                            
+
                                            
+
                                            
+__init__()[source]
                                            
+
                                            
+
+
                                            
+
                                            
+f(x)[source]
                                            
+
                                            Compute the objective function value.
                                            
+
                                            This method implements the sum of squares function.
                                            
+
                                            
+
                                            Parameters:
                                            
+
                                            x (list or numpy.ndarray) – The input chromosome represented as a list or array of real values.
                                            
+
                                            
+
                                            Returns:
                                            
+
                                            The computed value of the function given the input x.
                                            
+
                                            
+
                                            Return type:
                                            
+
                                            float
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_optimizer_alpha_cga.test_optimizer_alpha_cga_binary()[source]
                                            
+
                                            Test alpha_cga on a binary OneMax problem.
                                            
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_optimizer_alpha_cga.test_optimizer_alpha_cga_no_variation()[source]
                                            
+
                                            Test alpha_cga with no crossover or mutation.
                                            
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_optimizer_alpha_cga.test_optimizer_alpha_cga_real()[source]
                                            
+
                                            Test alpha_cga on a real-valued sum of squares problem.
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_optimizer_ccga module
                                            
+
                                            
+
                                            
+class pycellga.tests.test_optimizer_ccga.BinaryProblem[source]
                                            
+
                                            Bases: object
                                            
+
                                            Example problem class to be maximized for binary chromosomes.
                                            
+
                                            This class implements the OneMax problem where the goal is to maximize the number of 1s in a binary string.
                                            
+
                                            
+
                                            
+__init__()[source]
                                            
+
                                            
+
+
                                            
+
                                            
+f(x)[source]
                                            
+
                                            Compute the objective function value.
                                            
+
                                            This method counts the number of 1s in the binary chromosome.
                                            
+
                                            
+
                                            Parameters:
                                            
+
                                            x (list or numpy.ndarray) – The input chromosome represented as a list or array of binary values (0s and 1s).
                                            
+
                                            
+
                                            Returns:
                                            
+
                                            The computed value, which is the count of 1s in the chromosome.
                                            
+
                                            
+
                                            Return type:
                                            
+
                                            int
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_optimizer_ccga.test_optimizer_ccga_binary()[source]
                                            
+
                                            Test ccga on a binary OneMax problem.
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_optimizer_cga module
                                            
+
                                            
+
                                            
+class pycellga.tests.test_optimizer_cga.BinaryProblem[source]
                                            
+
                                            Bases: object
                                            
+
                                            Example problem class to be maximized for binary chromosomes.
                                            
+
                                            This class implements the OneMax problem where the goal is to maximize the number of 1s in a binary string.
                                            
+
                                            
+
                                            
+__init__()[source]
                                            
+
                                            
+
+
                                            
+
                                            
+f(x)[source]
                                            
+
                                            Compute the objective function value.
                                            
+
                                            This method counts the number of 1s in the binary chromosome.
                                            
+
                                            
+
                                            Parameters:
                                            
+
                                            x (list or numpy.ndarray) – The input chromosome represented as a list or array of binary values (0s and 1s).
                                            
+
                                            
+
                                            Returns:
                                            
+
                                            The computed value, which is the count of 1s in the chromosome.
                                            
+
                                            
+
                                            Return type:
                                            
+
                                            int
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
+
                                            
+
                                            
+class pycellga.tests.test_optimizer_cga.PermutationProblem(target: List[int])[source]
                                            
+
                                            Bases: object
                                            
+
                                            Example problem class to be minimized using a permutation-based approach.
                                            
+
                                            This class implements a simple objective function that measures the sum of absolute differences
+between the chromosome and a target permutation.
                                            
+
                                            
+
                                            
+__init__(target: List[int])[source]
                                            
+
                                            Initialize the PermutationProblem with a target permutation.
                                            
+
                                            
+
                                            Parameters:
                                            
+
                                            target (list of int) – The target permutation that the algorithm aims to find.
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+f(x: List[int]) → float[source]
                                            
+
                                            Compute the objective function value.
                                            
+
                                            This method implements the sum of absolute differences function.
                                            
+
                                            
+
                                            Parameters:
                                            
+
                                            x (list) – The input chromosome represented as a list of integers (permutation).
                                            
+
                                            
+
                                            Returns:
                                            
+
                                            The computed value of the function given the input x.
                                            
+
                                            
+
                                            Return type:
                                            
+
                                            float
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+test_optimizer_cga_permutation()[source]
                                            
+
                                            Test CGA on a permutation-based problem where the target is the identity permutation.
                                            
+
                                            
+
+
                                            
+
+
                                            
+
                                            
+class pycellga.tests.test_optimizer_cga.RealProblem[source]
                                            
+
                                            Bases: object
                                            
+
                                            Example problem class to be minimized.
                                            
+
                                            This class implements a simple sum of squares function with a global minimum value of 0,
+achieved when all elements of the chromosome are equal to 0.
                                            
+
                                            
+
                                            
+__init__()[source]
                                            
+
                                            
+
+
                                            
+
                                            
+f(x)[source]
                                            
+
                                            Compute the objective function value.
                                            
+
                                            This method implements the sum of squares function.
                                            
+
                                            
+
                                            Parameters:
                                            
+
                                            x (list or numpy.ndarray) – The input chromosome represented as a list or array of real values.
                                            
+
                                            
+
                                            Returns:
                                            
+
                                            The computed value of the function given the input x.
                                            
+
                                            
+
                                            Return type:
                                            
+
                                            float
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_optimizer_cga.test_optimizer_cga_binary()[source]
                                            
+
                                            Test CGA on a binary OneMax problem.
                                            
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_optimizer_cga.test_optimizer_cga_no_variation()[source]
                                            
+
                                            Test CGA with no crossover or mutation.
                                            
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_optimizer_cga.test_optimizer_cga_real()[source]
                                            
+
                                            Test CGA on a real-valued sum of squares problem.
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_optimizer_mccga module
                                            
+
                                            
+
                                            
+class pycellga.tests.test_optimizer_mccga.RealProblem[source]
                                            
+
                                            Bases: object
                                            
+
                                            Example problem class to be minimized.
                                            
+
                                            This class implements a simple sum of squares function with a global minimum value of 0,
+achieved when all elements of the chromosome are equal to 0.
                                            
+
                                            
+
                                            
+__init__()[source]
                                            
+
                                            
+
+
                                            
+
                                            
+f(x)[source]
                                            
+
                                            Compute the objective function value.
                                            
+
                                            This method implements the sum of squares function.
                                            
+
                                            
+
                                            Parameters:
                                            
+
                                            x (list or numpy.ndarray) – The input chromosome represented as a list or array of real values.
                                            
+
                                            
+
                                            Returns:
                                            
+
                                            The computed value of the function given the input x.
                                            
+
                                            
+
                                            Return type:
                                            
+
                                            float
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_optimizer_mccga.test_optimizer_mcccga_binary()[source]
                                            
+
                                            Test mcccga on a binary OneMax problem.
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_optimizer_sync_cga module
                                            
+
                                            
+
                                            
+class pycellga.tests.test_optimizer_sync_cga.BinaryProblem[source]
                                            
+
                                            Bases: object
                                            
+
                                            Example problem class to be maximized for binary chromosomes.
                                            
+
                                            This class implements the OneMax problem where the goal is to maximize the number of 1s in a binary string.
                                            
+
                                            
+
                                            
+__init__()[source]
                                            
+
                                            
+
+
                                            
+
                                            
+f(x)[source]
                                            
+
                                            Compute the objective function value.
                                            
+
                                            This method counts the number of 1s in the binary chromosome.
                                            
+
                                            
+
                                            Parameters:
                                            
+
                                            x (list or numpy.ndarray) – The input chromosome represented as a list or array of binary values (0s and 1s).
                                            
+
                                            
+
                                            Returns:
                                            
+
                                            The computed value, which is the count of 1s in the chromosome.
                                            
+
                                            
+
                                            Return type:
                                            
+
                                            int
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
+
                                            
+
                                            
+class pycellga.tests.test_optimizer_sync_cga.PermutationProblem(target: List[int])[source]
                                            
+
                                            Bases: object
                                            
+
                                            Example problem class to be minimized using a permutation-based approach.
                                            
+
                                            This class implements a simple objective function that measures the sum of absolute differences
+between the chromosome and a target permutation.
                                            
+
                                            
+
                                            
+__init__(target: List[int])[source]
                                            
+
                                            Initialize the PermutationProblem with a target permutation.
                                            
+
                                            
+
                                            Parameters:
                                            
+
                                            target (list of int) – The target permutation that the algorithm aims to find.
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+f(x: List[int]) → float[source]
                                            
+
                                            Compute the objective function value.
                                            
+
                                            This method implements the sum of absolute differences function.
                                            
+
                                            
+
                                            Parameters:
                                            
+
                                            x (list) – The input chromosome represented as a list of integers (permutation).
                                            
+
                                            
+
                                            Returns:
                                            
+
                                            The computed value of the function given the input x.
                                            
+
                                            
+
                                            Return type:
                                            
+
                                            float
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+test_optimizer_sync_cga_permutation()[source]
                                            
+
                                            Test sync_cga on a permutation-based problem where the target is the identity permutation.
                                            
+
                                            
+
+
                                            
+
+
                                            
+
                                            
+class pycellga.tests.test_optimizer_sync_cga.RealProblem[source]
                                            
+
                                            Bases: object
                                            
+
                                            Example problem class to be minimized.
                                            
+
                                            This class implements a simple sum of squares function with a global minimum value of 0,
+achieved when all elements of the chromosome are equal to 0.
                                            
+
                                            
+
                                            
+__init__()[source]
                                            
+
                                            
+
+
                                            
+
                                            
+f(x)[source]
                                            
+
                                            Compute the objective function value.
                                            
+
                                            This method implements the sum of squares function.
                                            
+
                                            
+
                                            Parameters:
                                            
+
                                            x (list or numpy.ndarray) – The input chromosome represented as a list or array of real values.
                                            
+
                                            
+
                                            Returns:
                                            
+
                                            The computed value of the function given the input x.
                                            
+
                                            
+
                                            Return type:
                                            
+
                                            float
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_optimizer_sync_cga.test_optimizer_sync_cga_binary()[source]
                                            
+
                                            Test sync_cga on a binary OneMax problem.
                                            
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_optimizer_sync_cga.test_optimizer_sync_cga_no_variation()[source]
                                            
+
                                            Test sync_cga with no crossover or mutation.
                                            
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_optimizer_sync_cga.test_optimizer_sync_cga_real()[source]
                                            
+
                                            Test sync_cga on a real-valued sum of squares problem.
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_peak module
                                            
+
                                            
+
                                            
+pycellga.tests.test_peak.peak_instance()[source]
                                            
+
                                            Fixture for creating an instance of the Peak class.
                                            
+
                                            This fixture returns an instance of the Peak class to be used in tests.
                                            
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_peak.test_peak(peak_instance)[source]
                                            
+
                                            Test the Peak function implementation.
                                            
+
                                            This test checks the calculation of the Peak fitness value for given lists of binary variables.
+It uses predefined inputs and compares the outputs to the expected values.
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_pmx_crossover module
                                            
+
                                            
+
                                            
+pycellga.tests.test_pmx_crossover.test_pmx_crossover()[source]
                                            
+
                                            Test the PMXCrossover class for generating offspring from two permutation parents.
                                            
+
                                            This test verifies that the PMXCrossover class correctly performs partially matched crossover
+(PMX) between two parent individuals. It ensures that:
+1. The offspring generated have the same chromosome size as the parents.
                                            
+
                                            The test performs the following checks:
+- Both offspring have the same chromosome size as the parents.
                                            
+
                                            
+
                                            Raises:
                                            
+
                                            AssertionError – If any of the conditions for the crossover operation are not met.
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_population module
                                            
+
                                            
+
                                            
+class pycellga.tests.test_population.MockProblem[source]
                                            
+
                                            Bases: AbstractProblem
                                            
+
                                            A mock problem for testing purposes.
                                            
+
                                            
+
                                            
+f(chromosome: List[float]) → float[source]
                                            
+
                                            Returns the sum of the chromosome as the fitness value.
                                            
+
                                            
+
+
                                            
+
                                            
+f(chromosome: List[float]) → float[source]
                                            
+
                                            Evaluate the fitness of a given solution x.
                                            
+
                                            
+
                                            Parameters:
                                            
+
                                            x (list) – A list representing a candidate solution.
                                            
+
                                            
+
                                            Returns:
                                            
+
                                            The fitness value of the candidate solution.
                                            
+
                                            
+
                                            Return type:
                                            
+
                                            float
                                            
+
                                            
+
                                            Raises:
                                            
+
                                            NotImplementedError – If the method is not implemented by a subclass.
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_population.setup_population()[source]
                                            
+
                                            Fixture for setting up a Population object.
                                            
+
                                            
+
                                            Returns:
                                            
+
                                            A population instance with a mock problem.
                                            
+
                                            
+
                                            Return type:
                                            
+
                                            Population
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_population.test_fitness_evaluation(setup_population)[source]
                                            
+
                                            Test the fitness evaluation of the individuals.
                                            
+
                                            
+
                                            Parameters:
                                            
+
                                              
+
                                            • setup_population (Population) – The population fixture.
                                              • 
+
                                            • Asserts
                                              • 
+
                                            • -------
                                              • 
+
                                            • performed. (True if the fitness evaluation is correctly)
                                              • 
+
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_population.test_initial_population_size(setup_population)[source]
                                            
+
                                            Test the size of the initial population.
                                            
+
                                            
+
                                            Parameters:
                                            
+
                                              
+
                                            • setup_population (Population) – The population fixture.
                                              • 
+
                                            • Asserts
                                              • 
+
                                            • -------
                                              • 
+
                                            • correct. (True if the size of the population is)
                                              • 
+
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_population.test_neighborhood_assignment(setup_population)[source]
                                            
+
                                            Test the neighborhood assignment for the individuals.
                                            
+
                                            
+
                                            Parameters:
                                            
+
                                              
+
                                            • setup_population (Population) – The population fixture.
                                              • 
+
                                            • Asserts
                                              • 
+
                                            • -------
                                              • 
+
                                            • assigned. (True if neighborhood positions are correctly)
                                              • 
+
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_pow_function module
                                            
+
                                            
+
                                            
+class pycellga.tests.test_pow_function.Pow[source]
                                            
+
                                            Bases: AbstractProblem
                                            
+
                                            Pow function implementation for optimization problems.
                                            
+
                                            The Pow function is widely used for testing optimization algorithms.
+The function is usually evaluated on the hypercube x_i ∈ [-5.0, 15.0].
                                            
+
                                            
+
                                            
+None
                                            
+
                                            
+
+
                                            
+
                                            
+f(x: list) → float[source]
                                            
+
                                            Calculates the Pow function value for a given list of variables.
                                            
+
                                            
+
+
                                            Notes
                                            
+
                                            -5.0 ≤ xi ≤ 15.0
+Global minimum at f(5, 7, 9, 3, 2) = 0
                                            
+
                                            
+
                                            
+f(x: list) → float[source]
                                            
+
                                            Calculate the Pow function value for a given list of variables.
                                            
+
                                            
+
                                            Parameters:
                                            
+
                                            x (list) – A list of float variables.
                                            
+
                                            
+
                                            Returns:
                                            
+
                                            The Pow function value.
                                            
+
                                            
+
                                            Return type:
                                            
+
                                            float
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_powell_function module
                                            
+
                                            
+
                                            
+pycellga.tests.test_powell_function.setup_powell()[source]
                                            
+
                                            Fixture to provide an instance of the Powell problem.
                                            
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_powell_function.test_powell_function(setup_powell)[source]
                                            
+
                                            Test the Powell function implementation.
                                            
+
                                            This test checks the calculation of the Powell function value for given lists of float variables.
+It uses predefined inputs and compares the outputs to the expected values.
                                            
+
                                            
+
                                            Parameters:
                                            
+
                                            setup_powell (fixture) – The fixture providing the Powell problem instance.
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_rastrigin module
                                            
+
                                            
+
                                            
+pycellga.tests.test_rastrigin.test_rastrigin()[source]
                                            
+
                                            Test the Rastrigin function implementation.
                                            
+
                                            This test verifies the calculation of the Rastrigin function value for a given list of continuous variables.
+It compares the function output with known expected values.
                                            
+
                                            Notes
                                            
+
                                            The Rastrigin function is a well-known benchmark function used in optimization problems.
+It has a global minimum value of 0, which is achieved when all variables are 0.
                                            
+
                                            
+
                                            Assertions
                                            
+
                                              
+
                                            • The function should return the correct values for predefined inputs.
                                              • 
+
                                            • The function should return 0 for an input list of all zeros.
                                              • 
+
                                            
+
                                            Examples
                                            
+
                                            >>> test_rastrigin()
+
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_rosenbrock module
                                            
+
                                            
+
                                            
+pycellga.tests.test_rosenbrock.test_rosenbrock()[source]
                                            
+
                                            Test the Rosenbrock function implementation.
                                            
+
                                            This test verifies the calculation of the Rosenbrock function value for a given list of continuous variables.
+It compares the function output with known expected values.
                                            
+
                                            Notes
                                            
+
                                            The Rosenbrock function, also known as the Rosenbrock’s valley or Rosenbrock’s banana function,
+is a common test problem for optimization algorithms. The global minimum is at (1, …, 1), where the function value is 0.
                                            
+
                                            
+
                                            Assertions
                                            
+
                                              
+
                                            • The function should return the correct values for predefined inputs.
                                              • 
+
                                            • The function should return 0 for an input list of all ones.
                                              • 
+
                                            
+
                                            Examples
                                            
+
                                            >>> test_rosenbrock()
+
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_rothellipsoid_function module
                                            
+
                                            
+
                                            
+pycellga.tests.test_rothellipsoid_function.setup_rothellipsoid()[source]
                                            
+
                                            Fixture to provide an instance of the Rotated Hyper-Ellipsoid problem.
                                            
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_rothellipsoid_function.test_rothellipsoid_function(setup_rothellipsoid)[source]
                                            
+
                                            Test the Rotated Hyper-Ellipsoid function implementation.
                                            
+
                                            This test checks the calculation of the Rotated Hyper-Ellipsoid function value for given lists of float variables.
+It uses predefined inputs and compares the outputs to the expected values.
                                            
+
                                            
+
                                            Parameters:
                                            
+
                                            setup_rothellipsoid (fixture) – The fixture providing the Rotated Hyper-Ellipsoid problem instance.
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_roulette_wheel_selection module
                                            
+
                                            
+
                                            
+pycellga.tests.test_roulette_wheel_selection.test_roulette_wheel_selection()[source]
                                            
+
                                            Test the RouletteWheelSelection class implementation.
                                            
+
                                            This test verifies the functionality of the RouletteWheelSelection for selecting parent individuals
+from a population. It ensures that the selected parents have valid attributes and different chromosomes
+and positions.
                                            
+
                                            The test performs the following checks:
+1. Each selected parent has the correct chromosome size and a non-None fitness value.
+2. Each selected parent has valid types for neighbors_positions and position attributes.
+3. The selected parents have different chromosomes and positions.
                                            
+
                                            
+
                                            Raises:
                                            
+
                                            AssertionError – If any of the conditions for the parent selection are not met.
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_schaffer2_function module
                                            
+
                                            
+
                                            
+pycellga.tests.test_schaffer2_function.setup_schaffer2()[source]
                                            
+
                                            Fixture to provide an instance of the Schaffer2 problem.
                                            
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_schaffer2_function.test_schaffer2_function(setup_schaffer2)[source]
                                            
+
                                            Test the Modified Schaffer function #2 implementation.
                                            
+
                                            This test checks the calculation of the Modified Schaffer function #2 value
+for given lists of float variables. It uses predefined inputs and compares
+the outputs to the expected values.
                                            
+
                                            
+
                                            Parameters:
                                            
+
                                            setup_schaffer2 (fixture) – The fixture providing the Schaffer2 problem instance.
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_schaffer_function module
                                            
+
                                            
+
                                            
+pycellga.tests.test_schaffer_function.setup_schaffer()[source]
                                            
+
                                            Fixture to provide an instance of the Schaffer problem.
                                            
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_schaffer_function.test_schaffer_function(setup_schaffer)[source]
                                            
+
                                            Test the Modified Schaffer function #1 implementation.
                                            
+
                                            This test checks the calculation of the Modified Schaffer function #1 value for given lists of float variables.
+It uses predefined inputs and compares the outputs to the expected values.
                                            
+
                                            
+
                                            Parameters:
                                            
+
                                            setup_schaffer (fixture) – The fixture providing the Schaffer problem instance.
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_schwefel module
                                            
+
                                            
+
                                            
+pycellga.tests.test_schwefel.test_schwefel()[source]
                                            
+
                                            Test the Schwefel function implementation.
                                            
+
                                            This test verifies the functionality of the Schwefel function on a set of predefined input values.
                                            
+
                                            The Schwefel function is used as a benchmark in optimization problems, and this test ensures
+that the function computes the expected results for given inputs.
                                            
+
                                            It performs assertions to check:
+- The correctness of the function’s output for specific input values.
+- The proper rounding of the function’s output to three decimal places.
                                            
+
                                            Notes
                                            
+
                                            The Schwefel function is typically used to evaluate optimization algorithms, and the expected values
+for the test cases are specific to the known behavior of the function.
                                            
+
                                            Examples
                                            
+
                                            >>> test_schwefel()
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_shuffle_mutation module
                                            
+
                                            
+
                                            
+pycellga.tests.test_shuffle_mutation.test_shuffle_mutation()[source]
                                            
+
                                            Test the ShuffleMutation class for the Individual class on the TSP problem.
                                            
+
                                            This test verifies that the ShuffleMutation correctly mutates the chromosome of
+an Individual by shuffling the elements. It ensures that:
+1. The chromosome is mutated.
+2. The chromosome size remains unchanged.
                                            
+
                                            The function initializes the chromosome with a random permutation of integers
+from 1 to CHSIZE and applies the shuffle mutation.
                                            
+
                                            Notes
                                            
+
                                            The test assumes that the ShuffleMutation class correctly implements shuffle
+mutation and that the TSP problem correctly evaluates the fitness of an individual.
                                            
+
                                            The following assertions are made:
+- At least one element in the chromosome is changed.
+- The size of the mutated individual’s chromosome matches the original size.
                                            
+
                                            
+
                                            Raises:
                                            
+
                                            AssertionError – If any of the conditions for correctness are not met.
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_sphere module
                                            
+
                                            
+
                                            
+pycellga.tests.test_sphere.test_sphere()[source]
                                            
+
                                            Test the Sphere function implementation.
                                            
+
                                            This test checks the calculation of the Sphere function value for given input values.
                                            
+
                                            The Sphere function is a common benchmark function in optimization, where the global minimum is
+at f(0, 0, …, 0) = 0. This test ensures that the function computes the correct results for specific
+input values and verifies that the function behaves as expected.
                                            
+
                                            It performs assertions to validate:
+- The correctness of the function’s output for specific test inputs.
+- The rounding of the function’s output to three decimal places.
                                            
+
                                            Examples
                                            
+
                                            >>> test_sphere()
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_styblinskitang_function module
                                            
+
                                            
+
                                            
+pycellga.tests.test_styblinskitang_function.setup_styblinski_tang()[source]
                                            
+
                                            Fixture to provide an instance of the StyblinskiTang problem.
                                            
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_styblinskitang_function.test_styblinskitang_function(setup_styblinski_tang)[source]
                                            
+
                                            Test the Styblinski-Tang function implementation.
                                            
+
                                            This test checks the calculation of the Styblinski-Tang function value
+for given lists of float variables. It uses predefined inputs and compares
+the outputs to the expected values.
                                            
+
                                            
+
                                            Parameters:
                                            
+
                                            setup_styblinski_tang (fixture) – The fixture providing the StyblinskiTang problem instance.
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_sumofdifferentpowers_function module
                                            
+
                                            
+
                                            
+pycellga.tests.test_sumofdifferentpowers_function.setup_sumofdifferentpowers()[source]
                                            
+
                                            Fixture to create an instance of the Sumofdifferentpowers problem.
                                            
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_sumofdifferentpowers_function.test_sumofdifferentpowers_function(setup_sumofdifferentpowers)[source]
                                            
+
                                            Test the Sum of Different Powers function implementation.
                                            
+
                                            This test checks the calculation of the Sum of Different Powers function value
+for given lists of float variables. It uses predefined inputs and compares
+the outputs to the expected values.
                                            
+
                                            
+
                                            Parameters:
                                            
+
                                            setup_sumofdifferentpowers (fixture) – The fixture providing the Sumofdifferentpowers problem instance.
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_swap_mutation module
                                            
+
                                            
+
                                            
+pycellga.tests.test_swap_mutation.test_swap_mutation()[source]
                                            
+
                                            Test the SwapMutation class for the Individual class on the TSP problem.
                                            
+
                                            This test verifies that the SwapMutation correctly mutates the chromosome of
+an Individual by swapping two elements. It ensures that:
+1. The chromosome is mutated.
+2. The chromosome size remains unchanged.
                                            
+
                                            The function initializes the chromosome with a random permutation of integers
+from 1 to CHSIZE and applies the swap mutation.
                                            
+
                                            Notes
                                            
+
                                            The test assumes that the SwapMutation class correctly implements swap mutation
+and that the TSP problem correctly evaluates the fitness of an individual.
                                            
+
                                            The following assertions are made:
+- At least one element in the chromosome is changed.
+- The size of the mutated individual’s chromosome matches the original size.
                                            
+
                                            
+
                                            Raises:
                                            
+
                                            AssertionError – If any of the conditions for correctness are not met.
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_threehumps_function module
                                            
+
                                            
+
                                            
+pycellga.tests.test_threehumps_function.setup_threehumps()[source]
                                            
+
                                            Fixture to provide the Threehumps problem instance.
                                            
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_threehumps_function.test_threehumps_function(setup_threehumps)[source]
                                            
+
                                            Test the Three Hump Camel function implementation.
                                            
+
                                            This test checks the calculation of the Three Hump Camel function value
+for given lists of float variables. It uses predefined inputs and compares
+the outputs to the expected values.
                                            
+
                                            
+
                                            Parameters:
                                            
+
                                            setup_threehumps (fixture) – The fixture providing the Threehumps problem instance.
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_tournament_selection module
                                            
+
                                            
+
                                            
+pycellga.tests.test_tournament_selection.test_tournament_selection()[source]
                                            
+
                                            Test the TournamentSelection class implementation.
                                            
+
                                            This test verifies the functionality of the TournamentSelection for selecting parent individuals
+from a population. It ensures that the selected parents have valid attributes and different chromosomes
+and positions.
                                            
+
                                            The test performs the following checks:
+1. Each selected parent has the correct chromosome size and a non-None fitness value.
+2. Each selected parent has valid types for neighbors_positions and position attributes.
+3. The selected parents have different chromosomes and positions.
                                            
+
                                            
+
                                            Parameters:
                                            
+
                                            None
                                            
+
                                            
+
                                            Raises:
                                            
+
                                            AssertionError – If any of the conditions for the parent selection are not met.
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_tsp module
                                            
+
                                            
+
                                            
+pycellga.tests.test_tsp.test_tsp()[source]
                                            
+
                                            Test the Tsp function implementation.
                                            
+
                                            This test verifies the calculation of the TSP (Traveling Salesman Problem) function value for
+different permutations of cities.
                                            
+
                                            The TSP function evaluates the total distance for a given permutation of cities. This test checks
+if the function computes the correct distance for specific permutations generated using different
+random seeds.
                                            
+
                                            Examples
                                            
+
                                            >>> test_tsp()
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_two_opt_mutation module
                                            
+
                                            
+
                                            
+pycellga.tests.test_two_opt_mutation.test_two_opt_mutation()[source]
                                            
+
                                            Test the TwoOptMutation class for the Individual class on the TSP problem.
                                            
+
                                            This test verifies that the TwoOptMutation correctly mutates the chromosome of
+an Individual by applying the 2-opt mutation technique. It ensures that:
+1. The chromosome is mutated by swapping two edges in the permutation.
+2. The chromosome size remains unchanged.
                                            
+
                                            The function initializes the chromosome with a random permutation of integers
+from 1 to CHSIZE and applies the two-opt mutation.
                                            
+
                                            Notes
                                            
+
                                            The test assumes that the TwoOptMutation class correctly implements the 2-opt mutation
+and that the TSP problem correctly evaluates the fitness of an individual.
                                            
+
                                            The following assertions are made:
+- At least one element in the chromosome is changed.
+- The size of the mutated individual’s chromosome matches the original size.
                                            
+
                                            
+
                                            Raises:
                                            
+
                                            AssertionError – If any of the conditions for correctness are not met.
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_two_point_crossover module
                                            
+
                                            
+
                                            
+pycellga.tests.test_two_point_crossover.test_two_point_crossover()[source]
                                            
+
                                            Test the TwoPointCrossover class implementation.
                                            
+
                                            This test verifies the functionality of the TwoPointCrossover class for binary chromosomes.
+It ensures that the crossover operation produces valid offspring with the expected chromosome size.
                                            
+
                                            The test performs the following checks:
+1. Both offspring have the same chromosome size as the parents.
+2. The chromosomes of the offspring contain only valid binary values (0 or 1).
                                            
+
                                            
+
                                            Raises:
                                            
+
                                            AssertionError – If any of the conditions for the crossover operation are not met.
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_unfair_average_crossover module
                                            
+
                                            
+
                                            
+class pycellga.tests.test_unfair_average_crossover.MockProblem[source]
                                            
+
                                            Bases: AbstractProblem
                                            
+
                                            A mock problem class for testing purposes.
                                            
+
                                            
+
                                            
+f(x: list) → float[source]
                                            
+
                                            A mock fitness function that simply sums the chromosome values.
                                            
+
                                            
+
                                            Parameters:
                                            
+
                                            x (list) – A list of float variables.
                                            
+
                                            
+
                                            Returns:
                                            
+
                                            The sum of the list values.
                                            
+
                                            
+
                                            Return type:
                                            
+
                                            float
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_unfair_average_crossover.setup_parents()[source]
                                            
+
                                            Fixture for creating a pair of parent Individual instances.
                                            
+
                                            
+
                                            Returns:
                                            
+
                                            A list containing two parent individuals with predefined chromosomes and size.
                                            
+
                                            
+
                                            Return type:
                                            
+
                                            list
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_unfair_average_crossover.setup_problem()[source]
                                            
+
                                            Fixture for creating a mock problem instance.
                                            
+
                                            
+
                                            Returns:
                                            
+
                                            An instance of the mock problem.
                                            
+
                                            
+
                                            Return type:
                                            
+
                                            MockProblem
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_unfair_average_crossover.test_unfair_average_crossover(setup_parents, setup_problem)[source]
                                            
+
                                            Test the UnfairAvarageCrossover function implementation.
                                            
+
                                            This test checks the unfair average crossover on a pair of parent individuals by verifying the recombination
+operation and the integrity of the offspring chromosomes.
                                            
+
                                            
+
                                            Parameters:
                                            
+
                                              
+
                                            • setup_parents (fixture) – The fixture providing the parent individuals.
                                              • 
+
                                            • setup_problem (fixture) – The fixture providing the mock problem instance.
                                              • 
+
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_uniform_crossover module
                                            
+
                                            
+
                                            
+pycellga.tests.test_uniform_crossover.test_uniform_crossover()[source]
                                            
+
                                            Test the UniformCrossover class implementation.
                                            
+
                                            This test verifies the functionality of the UniformCrossover class for binary chromosomes.
+It ensures that the crossover operation produces valid offspring with the expected chromosome size.
                                            
+
                                            The test performs the following checks:
+1. Both offspring have the same chromosome size as the parents.
+2. The chromosomes of the offspring contain only valid binary values (0 or 1).
                                            
+
                                            
+
                                            Raises:
                                            
+
                                            AssertionError – If any of the conditions for the crossover operation are not met.
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_zakharov_function module
                                            
+
                                            
+
                                            
+pycellga.tests.test_zakharov_function.test_zakharov_function()[source]
                                            
+
                                            Test the Zakharov function implementation.
                                            
+
                                            This test checks the calculation of the Zakharov function value
+for given lists of float variables. It uses predefined inputs and compares
+the outputs to the expected values.
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            pycellga.tests.test_zettle_function module
                                            
+
                                            
+
                                            
+pycellga.tests.test_zettle_function.setup_zettle()[source]
                                            
+
                                            Fixture to provide an instance of the Zettle problem.
                                            
+
                                            
+
+
                                            
+
                                            
+pycellga.tests.test_zettle_function.test_zettle_function(setup_zettle)[source]
                                            
+
                                            Test the Zettle function implementation.
                                            
+
                                            This test checks the calculation of the Zettle function value for given lists of float variables.
+It uses predefined inputs and compares the outputs to the expected values.
                                            
+
                                            
+
                                            Parameters:
                                            
+
                                            setup_zettle (fixture) – The fixture providing the Zettle problem instance.
                                            
+
                                            
+
                                            
+
                                            
+
+
                                            
+
                                            
+
                                            Module contents
                                            
+
                                            
+
                                            
+
+
+           
                                            
+          
                                            
+          
+        
                                            
+      
                                            
+    
                                            
+  
                                            
+   
+
+
+
\ No newline at end of file
diff --git a/searchindex.js b/searchindex.js
index b949842..97c29a5 100644
--- a/searchindex.js
+++ b/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"alltitles": {"Assertions": [[14, "assertions"], [14, "id2"], [14, "id3"]], "Contents:": [[0, null]], "Module contents": [[2, "module-src"], [3, "module-src.example"], [4, "module-src.mutation"], [5, "module-src.neighborhoods"], [6, "module-src.problems"], [7, "module-src.problems.single_objective"], [8, "module-src.problems.single_objective.continuous"], [9, "module-src.problems.single_objective.discrete"], [10, "module-src.problems.single_objective.discrete.binary"], [11, "module-src.problems.single_objective.discrete.permutation"], [12, "module-src.recombination"], [13, "module-src.selection"], [14, "module-src.tests"]], "PYCELLGA Documentation": [[0, null]], "Submodules": [[2, "submodules"], [3, "submodules"], [4, "submodules"], [5, "submodules"], [6, "submodules"], [8, "submodules"], [10, "submodules"], [11, "submodules"], [12, "submodules"], [13, "submodules"], [14, "submodules"]], "Subpackages": [[2, "subpackages"], [6, "subpackages"], [7, "subpackages"], [9, 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\ No newline at end of file
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+  setup module — PYCELLGA Documentation 1.0.0 documentation
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+
+  
                                            
+    
                                            © Copyright 2024, SEVGİ AKTEN KARAKAYA, MEHMET HAKAN SATMAN.
                                            
+  
                                            
+
+  Built with Sphinx using a
+    theme
+    provided by Read the Docs.
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                                            Submodules
                                            
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                                            src.byte_operators module
                                            
-
                                            
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-src.byte_operators.bits_to_float(bit_list: list[int]) → float[source]
                                            
-
                                            Convert a bit representation to its float value.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                            bit_list (list of int) – A list of 32 integers (0 or 1) representing the bit pattern of the float.
                                            
-
                                            
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                                            Returns:
                                            
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                                            The float value represented by the bit pattern.
                                            
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-src.byte_operators.bits_to_floats(bit_list: list[int]) → list[float][source]
                                            
-
                                            Convert a combined bit representation back to a list of floats.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                            bit_list (list of int) – A list of integers (0 or 1) representing the combined bit patterns of the floats.
                                            
-
                                            
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                                            Returns:
                                            
-
                                            The list of float values represented by the bit pattern.
                                            
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                                            list of float
                                            
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-src.byte_operators.float_to_bits(float_number: float) → list[int][source]
                                            
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                                            Convert a float to its bit representation.
                                            
-
                                            
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                                            Parameters:
                                            
-
                                            float_number (float) – The float number to be converted.
                                            
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                                            A list of 32 integers (0 or 1) representing the bit pattern of the float.
                                            
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                                            list of int
                                            
-
                                            
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-
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-src.byte_operators.floats_to_bits(float_list: list[float]) → list[int][source]
                                            
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                                            Convert a list of floats to their combined bit representation.
                                            
-
                                            
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                                            Parameters:
                                            
-
                                            float_list (list of float) – The list of float numbers to be converted.
                                            
-
                                            
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                                            A list of integers (0 or 1) representing the combined bit patterns of the floats.
                                            
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                                            Return type:
                                            
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                                            list of int
                                            
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                                            src.grid module
                                            
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-class src.grid.Grid(n_rows: int, n_cols: int)[source]
                                            
-
                                            Bases: object
                                            
-
                                            A class to represent a 2D grid.
                                            
-
                                            
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                                            Number of rows in the grid.
                                            
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                                            Type:
                                            
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                                            int
                                            
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                                            Number of columns in the grid.
                                            
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                                            Type:
                                            
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-__init__(n_rows: int, n_cols: int)[source]
                                            
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                                            Initialize the Grid with the number of rows and columns.
                                            
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                                            Parameters:
                                            
-
                                              
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                                            • n_rows (int) – Number of rows in the grid.
                                              • 
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                                            • n_cols (int) – Number of columns in the grid.
                                              • 
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-make_2d_grid() → list[source]
                                            
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                                            Create a 2D grid where each cell is represented by a tuple (row, column).
                                            
-
                                            
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                                            Returns:
                                            
-
                                            A list of tuples where each tuple represents a grid cell.
-Each tuple is of the form (row, column), with rows and columns starting from 1.
                                            
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                                            Return type:
                                            
-
                                            list
                                            
-
                                            
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                                            src.individual module
                                            
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                                            src.optimizer module
                                            
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                                            src.population module
                                            
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                                            Module contents
                                            
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diff --git a/src.mutation.html b/src.mutation.html
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-  src.mutation package — PYCELLGA Documentation 1.0.0 documentation
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                                            src.mutation package
                                            
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                                            Submodules
                                            
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                                            src.mutation.bit_flip_mutation module
                                            
-
                                            
-
                                            
-class src.mutation.bit_flip_mutation.BitFlipMutation(mutation_cand: Individual = None, problem: AbstractProblem = None)[source]
                                            
-
                                            Bases: MutationOperator
                                            
-
                                            BitFlipMutation performs a bit flip mutation on an individual in a Genetic Algorithm.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                              
-
                                            • mutation_cand (Individual, optional) – The candidate individual to be mutated (default is None).
                                              • 
-
                                            • problem (AbstractProblem, optional) – The problem instance that provides the fitness function (default is None).
                                              • 
-
                                            
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-__init__(mutation_cand: Individual = None, problem: AbstractProblem = None)[source]
                                            
-
                                            Initialize the BitFlipMutation object.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                              
-
                                            • mutation_cand (Individual, optional) – The candidate individual to be mutated (default is None).
                                              • 
-
                                            • problem (AbstractProblem, optional) – The problem instance that provides the fitness function (default is None).
                                              • 
-
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-mutate() → Individual[source]
                                            
-
                                            Perform a bit flip mutation on the candidate individual.
                                            
-
                                            A single bit in the candidate’s chromosome is randomly selected and flipped
-(i.e., a 0 is changed to a 1, or a 1 is changed to a 0).
                                            
-
                                            
-
                                            Returns:
                                            
-
                                            A new individual with the mutated chromosome.
                                            
-
                                            
-
                                            Return type:
                                            
-
                                            Individual
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
-
                                            
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-
                                            src.mutation.byte_mutation module
                                            
-
                                            
-
                                            
-class src.mutation.byte_mutation.ByteMutation(mutation_cand: Individual = None, problem: AbstractProblem = None)[source]
                                            
-
                                            Bases: MutationOperator
                                            
-
                                            ByteMutation operator defined in (Satman, 2013). ByteMutation performs a byte-wise mutation
-on an individual’s chromosome in a Genetic Algorithm.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                              
-
                                            • mutation_cand (Individual, optional) – The candidate individual to be mutated (default is None).
                                              • 
-
                                            • problem (AbstractProblem, optional) – The problem instance that provides the fitness function (default is None).
                                              • 
-
                                            
-
                                            
-
                                            
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-
                                            
-__init__(mutation_cand: Individual = None, problem: AbstractProblem = None)[source]
                                            
-
                                            Initialize the ByteMutation object.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                              
-
                                            • mutation_cand (Individual, optional) – The candidate individual to be mutated (default is None).
                                              • 
-
                                            • problem (AbstractProblem, optional) – The problem instance that provides the fitness function (default is None).
                                              • 
-
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-mutate() → Individual[source]
                                            
-
                                            Perform a byte-wise mutation on the candidate individual.
                                            
-
                                            A single byte in one of the candidate’s chromosome’s floating-point numbers is randomly selected
-and either incremented or decremented by 1, wrapping around if necessary.
                                            
-
                                            
-
                                            Returns:
                                            
-
                                            A new individual with the mutated chromosome.
                                            
-
                                            
-
                                            Return type:
                                            
-
                                            Individual
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
-
                                            
-
                                            
-
                                            src.mutation.byte_mutation_random module
                                            
-
                                            
-
                                            
-class src.mutation.byte_mutation_random.ByteMutationRandom(mutation_cand: Individual = None, problem: AbstractProblem = None)[source]
                                            
-
                                            Bases: MutationOperator
                                            
-
                                            ByteMutationRandom operator defined in (Satman, 2013). ByteMutationRandom performs
-a random byte mutation on an individual’s chromosome in a Genetic Algorithm.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                              
-
                                            • mutation_cand (Individual, optional) – The candidate individual to be mutated (default is None).
                                              • 
-
                                            • problem (AbstractProblem, optional) – The problem instance that provides the fitness function (default is None).
                                              • 
-
                                            
-
                                            
-
                                            
-
                                            
-
                                            
-__init__(mutation_cand: Individual = None, problem: AbstractProblem = None)[source]
                                            
-
                                            Initialize the ByteMutationRandom object.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                              
-
                                            • mutation_cand (Individual, optional) – The candidate individual to be mutated (default is None).
                                              • 
-
                                            • problem (AbstractProblem, optional) – The problem instance that provides the fitness function (default is None).
                                              • 
-
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-mutate() → Individual[source]
                                            
-
                                            Perform a random byte mutation on the candidate individual.
                                            
-
                                            A single byte in one of the candidate’s chromosome’s floating-point numbers is randomly selected
-and mutated to a random value.
                                            
-
                                            
-
                                            Returns:
                                            
-
                                            A new individual with the mutated chromosome.
                                            
-
                                            
-
                                            Return type:
                                            
-
                                            Individual
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
-
                                            
-
                                            
-
                                            src.mutation.float_uniform_mutation module
                                            
-
                                            
-
                                            
-class src.mutation.float_uniform_mutation.FloatUniformMutation(mutation_cand: Individual = None, problem: AbstractProblem = None)[source]
                                            
-
                                            Bases: MutationOperator
                                            
-
                                            FloatUniformMutation performs a uniform mutation on an individual’s chromosome in a Genetic Algorithm.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                              
-
                                            • mutation_cand (Individual, optional) – The candidate individual to be mutated (default is None).
                                              • 
-
                                            • problem (AbstractProblem, optional) – The problem instance that provides the fitness function (default is None).
                                              • 
-
                                            
-
                                            
-
                                            
-
                                            
-
                                            
-__init__(mutation_cand: Individual = None, problem: AbstractProblem = None)[source]
                                            
-
                                            Initialize the FloatUniformMutation object.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                              
-
                                            • mutation_cand (Individual, optional) – The candidate individual to be mutated (default is None).
                                              • 
-
                                            • problem (AbstractProblem, optional) – The problem instance that provides the fitness function (default is None).
                                              • 
-
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-mutate() → Individual[source]
                                            
-
                                            Perform a uniform mutation on the candidate individual.
                                            
-
                                            Each gene in the candidate’s chromosome is mutated by adding or subtracting a random float uniformly
-sampled from [0, 1]. The mutation is rounded to 5 decimal places.
                                            
-
                                            
-
                                            Returns:
                                            
-
                                            A new individual with the mutated chromosome.
                                            
-
                                            
-
                                            Return type:
                                            
-
                                            Individual
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
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-
                                            src.mutation.insertion_mutation module
                                            
-
                                            
-
                                            
-class src.mutation.insertion_mutation.InsertionMutation(mutation_cand: Individual = None, problem: AbstractProblem = None)[source]
                                            
-
                                            Bases: MutationOperator
                                            
-
                                            InsertionMutation performs an insertion mutation on an individual’s chromosome in a Genetic Algorithm.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                              
-
                                            • mutation_cand (Individual, optional) – The candidate individual to be mutated (default is None).
                                              • 
-
                                            • problem (AbstractProblem, optional) – The problem instance that provides the fitness function (default is None).
                                              • 
-
                                            
-
                                            
-
                                            
-
                                            
-
                                            
-__init__(mutation_cand: Individual = None, problem: AbstractProblem = None)[source]
                                            
-
                                            Initialize the InsertionMutation object.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                              
-
                                            • mutation_cand (Individual, optional) – The candidate individual to be mutated (default is None).
                                              • 
-
                                            • problem (AbstractProblem, optional) – The problem instance that provides the fitness function (default is None).
                                              • 
-
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-mutate() → Individual[source]
                                            
-
                                            Perform an insertion mutation on the candidate individual.
                                            
-
                                            A gene in the candidate’s chromosome is randomly selected and moved to a new position in the chromosome.
                                            
-
                                            
-
                                            Returns:
                                            
-
                                            A new individual with the mutated chromosome.
                                            
-
                                            
-
                                            Return type:
                                            
-
                                            Individual
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
-
                                            
-
                                            
-
                                            src.mutation.mutation_operator module
                                            
-
                                            
-
                                            
-class src.mutation.mutation_operator.MutationOperator[source]
                                            
-
                                            Bases: object
                                            
-
                                            
-
                                            
-mutate()[source]
                                            
-
                                            
-
-
                                            
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-
                                            src.mutation.shuffle_mutation module
                                            
-
                                            
-
                                            
-class src.mutation.shuffle_mutation.ShuffleMutation(mutation_cand: Individual = None, problem: AbstractProblem = None)[source]
                                            
-
                                            Bases: MutationOperator
                                            
-
                                            ShuffleMutation performs a shuffle mutation on an individual’s chromosome in a Genetic Algorithm.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                              
-
                                            • mutation_cand (Individual, optional) – The candidate individual to be mutated (default is None).
                                              • 
-
                                            • problem (AbstractProblem, optional) – The problem instance that provides the fitness function (default is None).
                                              • 
-
                                            
-
                                            
-
                                            
-
                                            
-
                                            
-__init__(mutation_cand: Individual = None, problem: AbstractProblem = None)[source]
                                            
-
                                            Initialize the ShuffleMutation object.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                              
-
                                            • mutation_cand (Individual, optional) – The candidate individual to be mutated (default is None).
                                              • 
-
                                            • problem (AbstractProblem, optional) – The problem instance that provides the fitness function (default is None).
                                              • 
-
                                            
-
                                            
-
                                            
-
                                            
-
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-
                                            
-mutate() → Individual[source]
                                            
-
                                            Perform a shuffle mutation on the candidate individual.
                                            
-
                                            A subsequence of genes in the candidate’s chromosome is randomly selected and shuffled.
                                            
-
                                            
-
                                            Returns:
                                            
-
                                            A new individual with the mutated chromosome.
                                            
-
                                            
-
                                            Return type:
                                            
-
                                            Individual
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
-
                                            
-
                                            
-
                                            src.mutation.swap_mutation module
                                            
-
                                            
-
                                            
-class src.mutation.swap_mutation.SwapMutation(mutation_cand: Individual = None, problem: AbstractProblem = None)[source]
                                            
-
                                            Bases: MutationOperator
                                            
-
                                            SwapMutation performs a swap mutation on an individual’s chromosome in a Genetic Algorithm.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                              
-
                                            • mutation_cand (Individual, optional) – The candidate individual to be mutated (default is None).
                                              • 
-
                                            • problem (AbstractProblem, optional) – The problem instance that provides the fitness function (default is None).
                                              • 
-
                                            
-
                                            
-
                                            
-
                                            
-
                                            
-__init__(mutation_cand: Individual = None, problem: AbstractProblem = None)[source]
                                            
-
                                            Initialize the SwapMutation object.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                              
-
                                            • mutation_cand (Individual, optional) – The candidate individual to be mutated (default is None).
                                              • 
-
                                            • problem (AbstractProblem, optional) – The problem instance that provides the fitness function (default is None).
                                              • 
-
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-mutate() → Individual[source]
                                            
-
                                            Perform a swap mutation on the candidate individual.
                                            
-
                                            Two genes in the candidate’s chromosome are randomly selected and swapped.
                                            
-
                                            
-
                                            Returns:
                                            
-
                                            A new individual with the mutated chromosome.
                                            
-
                                            
-
                                            Return type:
                                            
-
                                            Individual
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
-
                                            
-
                                            
-
                                            src.mutation.two_opt_mutation module
                                            
-
                                            
-
                                            
-class src.mutation.two_opt_mutation.TwoOptMutation(mutation_cand: Individual = None, problem: AbstractProblem = None)[source]
                                            
-
                                            Bases: MutationOperator
                                            
-
                                            TwoOptMutation performs a 2-opt mutation on an individual’s chromosome in a Genetic Algorithm.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                              
-
                                            • mutation_cand (Individual, optional) – The candidate individual to be mutated (default is None).
                                              • 
-
                                            • problem (AbstractProblem, optional) – The problem instance that provides the fitness function (default is None).
                                              • 
-
                                            
-
                                            
-
                                            
-
                                            
-
                                            
-__init__(mutation_cand: Individual = None, problem: AbstractProblem = None)[source]
                                            
-
                                            Initialize the TwoOptMutation object.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                              
-
                                            • mutation_cand (Individual, optional) – The candidate individual to be mutated (default is None).
                                              • 
-
                                            • problem (AbstractProblem, optional) – The problem instance that provides the fitness function (default is None).
                                              • 
-
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-mutate() → Individual[source]
                                            
-
                                            Perform a 2-opt mutation on the candidate individual.
                                            
-
                                            A segment of the candidate’s chromosome is randomly selected and reversed.
                                            
-
                                            
-
                                            Returns:
                                            
-
                                            A new individual with the mutated chromosome.
                                            
-
                                            
-
                                            Return type:
                                            
-
                                            Individual
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
-
                                            
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-
                                            Module contents
                                            
-
                                            
-
                                            
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diff --git a/src.problems.html b/src.problems.html
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-  src.problems package — PYCELLGA Documentation 1.0.0 documentation
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                                            Subpackages
                                            
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                                            src.problems.abstract_problem module
                                            
-
                                            
-
                                            
-class src.problems.abstract_problem.AbstractProblem[source]
                                            
-
                                            Bases: object
                                            
-
                                            An abstract base class for optimization problems.
                                            
-
                                            
-
                                            
-f(x)[source]
                                            
-
                                            Evaluates the fitness of a given solution x.
                                            
-
                                            
-
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-f(x)[source]
                                            
-
                                            Evaluate the fitness of a given solution x.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                            x (list) – A list representing a candidate solution.
                                            
-
                                            
-
                                            Returns:
                                            
-
                                            The fitness value of the candidate solution.
                                            
-
                                            
-
                                            Return type:
                                            
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                                            float
                                            
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-
                                            NotImplementedError – If the method is not implemented by a subclass.
                                            
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                                            src.problems.single_objective.discrete package
                                            
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                                            src.problems.single_objective package
                                            
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-      
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                                            src.tests package
                                            
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-
                                            Submodules
                                            
-
                                            
-
                                            
-
                                            src.tests.conftest module
                                            
-
                                            
-
                                            
-
                                            src.tests.test_ackley module
                                            
-
                                            
-
                                            
-src.tests.test_ackley.test_ackley()[source]
                                            
-
                                            Test the Ackley function implementation.
                                            
-
                                            This test verifies the correctness of the Ackley function by evaluating it at several points and
-comparing the results to expected values.
                                            
-
                                            The Ackley function is commonly used as a benchmark for optimization algorithms. It is a continuous
-function with multiple local minima and a single global minimum.
                                            
-
                                            The test performs the following checks:
-1. Evaluates the Ackley function at a set of given points.
-2. Compares the computed values to expected results.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                            None
                                            
-
                                            
-
                                            Raises:
                                            
-
                                            AssertionError – If any of the computed values do not match the expected values.
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_arithmetic_crossover module
                                            
-
                                            
-
                                            
-class src.tests.test_arithmetic_crossover.MockProblem[source]
                                            
-
                                            Bases: AbstractProblem
                                            
-
                                            A mock problem class for testing purposes.
                                            
-
                                            
-
                                            
-f(x: list) → float[source]
                                            
-
                                            A mock fitness function that simply sums the chromosome values.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                            x (list) – A list of float variables.
                                            
-
                                            
-
                                            Returns:
                                            
-
                                            The sum of the list values.
                                            
-
                                            
-
                                            Return type:
                                            
-
                                            float
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
-
                                            
-
                                            
-src.tests.test_arithmetic_crossover.setup_parents()[source]
                                            
-
                                            Fixture for creating a pair of parent Individual instances.
                                            
-
                                            
-
                                            Returns:
                                            
-
                                            A list containing two parent individuals with predefined chromosomes and size.
                                            
-
                                            
-
                                            Return type:
                                            
-
                                            list
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-src.tests.test_arithmetic_crossover.setup_problem()[source]
                                            
-
                                            Fixture for creating a mock problem instance.
                                            
-
                                            
-
                                            Returns:
                                            
-
                                            An instance of the mock problem.
                                            
-
                                            
-
                                            Return type:
                                            
-
                                            MockProblem
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-src.tests.test_arithmetic_crossover.test_arithmetic_crossover(setup_parents, setup_problem)[source]
                                            
-
                                            Test the ArithmeticCrossover function implementation.
                                            
-
                                            This test checks the arithmetic crossover on a pair of parent individuals by verifying the recombination
-operation and the integrity of the offspring chromosomes.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                              
-
                                            • setup_parents (fixture) – The fixture providing the parent individuals.
                                              • 
-
                                            • setup_problem (fixture) – The fixture providing the mock problem instance.
                                              • 
-
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_bentcigar_function module
                                            
-
                                            
-
                                            
-src.tests.test_bentcigar_function.setup_bentcigar()[source]
                                            
-
                                            Fixture for creating an instance of the Bentcigar problem.
                                            
-
                                            
-
                                            Returns:
                                            
-
                                            An instance of the Bentcigar problem.
                                            
-
                                            
-
                                            Return type:
                                            
-
                                            Bentcigar
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-src.tests.test_bentcigar_function.test_bentcigar_function(setup_bentcigar)[source]
                                            
-
                                            Test the Bentcigar function implementation.
                                            
-
                                            This test checks the calculation of the Bentcigar function value for given lists of float variables.
-It uses predefined inputs and compares the outputs to the expected values.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                            setup_bentcigar (fixture) – The fixture providing the Bentcigar problem instance.
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_bit_flip_mutation module
                                            
-
                                            
-
                                            
-src.tests.test_bit_flip_mutation.test_bit_flip_mutation()[source]
                                            
-
                                            Test the BitFlipMutation class for the Individual class on the OneMax problem.
                                            
-
                                            This test verifies that the BitFlipMutation correctly mutates the chromosome of
-an Individual by flipping some bits and ensures that the mutation is applied
-correctly. The test checks that:
-1. The chromosome is mutated.
-2. The chromosome size remains unchanged.
-3. The fitness value of the mutated individual is computed correctly.
                                            
-
                                            The function uses a fixed random seed to ensure reproducibility of the results.
                                            
-
                                            Notes
                                            
-
                                            The test assumes that the BitFlipMutation class correctly implements bit flipping
-mutation and that the OneMax problem correctly evaluates the fitness of an individual.
                                            
-
                                            The following assertions are made:
-- At least one bit in the chromosome is changed.
-- The size of the mutated individual’s chromosome matches the original size.
-- The fitness value of the mutated individual is calculated correctly.
                                            
-
                                            
-
                                            Raises:
                                            
-
                                            AssertionError – If any of the conditions for correctness are not met.
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_blxalpha_crossover module
                                            
-
                                            
-
                                            
-class src.tests.test_blxalpha_crossover.MockProblem[source]
                                            
-
                                            Bases: AbstractProblem
                                            
-
                                            A mock problem class for testing purposes.
                                            
-
                                            
-
                                            
-f(x: list) → float[source]
                                            
-
                                            A mock fitness function that simply sums the chromosome values.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                            x (list) – A list of float variables.
                                            
-
                                            
-
                                            Returns:
                                            
-
                                            The sum of the list values.
                                            
-
                                            
-
                                            Return type:
                                            
-
                                            float
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
-
                                            
-
                                            
-src.tests.test_blxalpha_crossover.setup_parents()[source]
                                            
-
                                            Fixture for creating a pair of parent Individual instances.
                                            
-
                                            
-
                                            Returns:
                                            
-
                                            A list containing two parent individuals with predefined chromosomes and size.
                                            
-
                                            
-
                                            Return type:
                                            
-
                                            list
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-src.tests.test_blxalpha_crossover.setup_problem()[source]
                                            
-
                                            Fixture for creating a mock problem instance.
                                            
-
                                            
-
                                            Returns:
                                            
-
                                            An instance of the mock problem.
                                            
-
                                            
-
                                            Return type:
                                            
-
                                            MockProblem
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-src.tests.test_blxalpha_crossover.test_blxalpha_crossover(setup_parents, setup_problem)[source]
                                            
-
                                            Test the BlxalphaCrossover function implementation.
                                            
-
                                            This test checks the BLX-alpha crossover on a pair of parent individuals by verifying the recombination
-operation and the integrity of the offspring chromosomes.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                              
-
                                            • setup_parents (fixture) – The fixture providing the parent individuals.
                                              • 
-
                                            • setup_problem (fixture) – The fixture providing the mock problem instance.
                                              • 
-
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_bohachevsky module
                                            
-
                                            
-
                                            
-src.tests.test_bohachevsky.test_bohachevsky()[source]
                                            
-
                                            Test the Bohachevsky function implementation.
                                            
-
                                            This test verifies the correctness of the Bohachevsky function by evaluating it at several points and
-comparing the results to expected values.
                                            
-
                                            The Bohachevsky function is used as a benchmark for optimization algorithms. It has multiple local minima
-and is designed to test the performance of optimization algorithms.
                                            
-
                                            The test performs the following checks:
-1. Evaluates the Bohachevsky function at specific points.
-2. Compares the computed values to the expected rounded results.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                            None
                                            
-
                                            
-
                                            Raises:
                                            
-
                                            AssertionError – If any of the computed values do not match the expected rounded values.
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_byte_mutation module
                                            
-
                                            
-
                                            
-class src.tests.test_byte_mutation.MockProblem[source]
                                            
-
                                            Bases: AbstractProblem
                                            
-
                                            A mock problem class for testing purposes.
                                            
-
                                            
-
                                            
-f(x: list) → float[source]
                                            
-
                                            A mock fitness function that simply sums the chromosome values.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                            x (list) – A list of float variables.
                                            
-
                                            
-
                                            Returns:
                                            
-
                                            The sum of the list values.
                                            
-
                                            
-
                                            Return type:
                                            
-
                                            float
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
-
                                            
-
                                            
-src.tests.test_byte_mutation.setup_individual()[source]
                                            
-
                                            Fixture for creating a sample Individual instance.
                                            
-
                                            
-
                                            Returns:
                                            
-
                                            An individual instance with a predefined chromosome and size.
                                            
-
                                            
-
                                            Return type:
                                            
-
                                            Individual
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-src.tests.test_byte_mutation.setup_problem()[source]
                                            
-
                                            Fixture for creating a mock problem instance.
                                            
-
                                            
-
                                            Returns:
                                            
-
                                            An instance of the mock problem.
                                            
-
                                            
-
                                            Return type:
                                            
-
                                            MockProblem
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-src.tests.test_byte_mutation.test_byte_mutation(setup_individual, setup_problem)[source]
                                            
-
                                            Test the ByteMutation function implementation.
                                            
-
                                            This test checks the byte-wise mutation on an individual’s chromosome by verifying the mutation
-operation and the integrity of the chromosome.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                              
-
                                            • setup_individual (fixture) – The fixture providing the sample individual.
                                              • 
-
                                            • setup_problem (fixture) – The fixture providing the mock problem instance.
                                              • 
-
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_byte_mutation_random module
                                            
-
                                            
-
                                            
-class src.tests.test_byte_mutation_random.MockProblem[source]
                                            
-
                                            Bases: AbstractProblem
                                            
-
                                            A mock problem class for testing purposes.
                                            
-
                                            
-
                                            
-f(x: list) → float[source]
                                            
-
                                            A mock fitness function that simply sums the chromosome values.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                            x (list) – A list of float variables.
                                            
-
                                            
-
                                            Returns:
                                            
-
                                            The sum of the list values.
                                            
-
                                            
-
                                            Return type:
                                            
-
                                            float
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
-
                                            
-
                                            
-src.tests.test_byte_mutation_random.setup_individual()[source]
                                            
-
                                            Fixture for creating a sample Individual instance.
                                            
-
                                            
-
                                            Returns:
                                            
-
                                            An individual instance with a predefined chromosome and size.
                                            
-
                                            
-
                                            Return type:
                                            
-
                                            Individual
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-src.tests.test_byte_mutation_random.setup_problem()[source]
                                            
-
                                            Fixture for creating a mock problem instance.
                                            
-
                                            
-
                                            Returns:
                                            
-
                                            An instance of the mock problem.
                                            
-
                                            
-
                                            Return type:
                                            
-
                                            MockProblem
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-src.tests.test_byte_mutation_random.test_byte_mutation_random(setup_individual, setup_problem)[source]
                                            
-
                                            Test the ByteMutationRandom function implementation.
                                            
-
                                            This test checks the byte mutation on an individual’s chromosome by verifying the mutation
-operation and the integrity of the chromosome.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                              
-
                                            • setup_individual (fixture) – The fixture providing the sample individual.
                                              • 
-
                                            • setup_problem (fixture) – The fixture providing the mock problem instance.
                                              • 
-
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_byte_one_point_crossover module
                                            
-
                                            
-
                                            
-class src.tests.test_byte_one_point_crossover.MockProblem[source]
                                            
-
                                            Bases: AbstractProblem
                                            
-
                                            A mock problem class for testing purposes.
                                            
-
                                            
-
                                            
-f(x: list) → float[source]
                                            
-
                                            A mock fitness function that simply sums the chromosome values.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                            x (list) – A list of float variables.
                                            
-
                                            
-
                                            Returns:
                                            
-
                                            The sum of the list values.
                                            
-
                                            
-
                                            Return type:
                                            
-
                                            float
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
-
                                            
-
                                            
-src.tests.test_byte_one_point_crossover.setup_parents()[source]
                                            
-
                                            Fixture for creating a pair of parent Individual instances.
                                            
-
                                            
-
                                            Returns:
                                            
-
                                            A list containing two parent individuals with predefined chromosomes and size.
                                            
-
                                            
-
                                            Return type:
                                            
-
                                            list
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-src.tests.test_byte_one_point_crossover.setup_problem()[source]
                                            
-
                                            Fixture for creating a mock problem instance.
                                            
-
                                            
-
                                            Returns:
                                            
-
                                            An instance of the mock problem.
                                            
-
                                            
-
                                            Return type:
                                            
-
                                            MockProblem
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-src.tests.test_byte_one_point_crossover.test_byte_one_point_crossover(setup_parents, setup_problem)[source]
                                            
-
                                            Test the ByteOnePointCrossover function implementation.
                                            
-
                                            This test checks the byte-level one-point crossover on a pair of parent individuals by verifying the recombination
-operation and the integrity of the offspring chromosomes.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                              
-
                                            • setup_parents (fixture) – The fixture providing the parent individuals.
                                              • 
-
                                            • setup_problem (fixture) – The fixture providing the mock problem instance.
                                              • 
-
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_byte_operators module
                                            
-
                                            
-
                                            
-src.tests.test_byte_operators.test_bits_to_float()[source]
                                            
-
                                            Test the bits_to_float function.
                                            
-
                                            
-
-
                                            
-
                                            
-src.tests.test_byte_operators.test_bits_to_floats()[source]
                                            
-
                                            Test the bits_to_floats function.
                                            
-
                                            
-
-
                                            
-
                                            
-src.tests.test_byte_operators.test_float_to_bits()[source]
                                            
-
                                            Test the float_to_bits function.
                                            
-
                                            
-
-
                                            
-
                                            
-src.tests.test_byte_operators.test_floats_to_bits()[source]
                                            
-
                                            Test the floats_to_bits function.
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_byte_uniform_crossover module
                                            
-
                                            
-
                                            
-class src.tests.test_byte_uniform_crossover.MockProblem[source]
                                            
-
                                            Bases: AbstractProblem
                                            
-
                                            A mock problem class for testing purposes.
                                            
-
                                            
-
                                            
-f(x: list) → float[source]
                                            
-
                                            A mock fitness function that simply sums the chromosome values.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                            x (list) – A list of float variables.
                                            
-
                                            
-
                                            Returns:
                                            
-
                                            The sum of the list values.
                                            
-
                                            
-
                                            Return type:
                                            
-
                                            float
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
-
                                            
-
                                            
-src.tests.test_byte_uniform_crossover.setup_parents()[source]
                                            
-
                                            Fixture for creating a pair of parent Individual instances.
                                            
-
                                            
-
                                            Returns:
                                            
-
                                            A list containing two parent individuals with predefined chromosomes and size.
                                            
-
                                            
-
                                            Return type:
                                            
-
                                            list
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-src.tests.test_byte_uniform_crossover.setup_problem()[source]
                                            
-
                                            Fixture for creating a mock problem instance.
                                            
-
                                            
-
                                            Returns:
                                            
-
                                            An instance of the mock problem.
                                            
-
                                            
-
                                            Return type:
                                            
-
                                            MockProblem
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-src.tests.test_byte_uniform_crossover.test_byte_uniform_crossover(setup_parents, setup_problem)[source]
                                            
-
                                            Test the ByteUniformCrossover function implementation.
                                            
-
                                            This test checks the byte-level uniform crossover on a pair of parent individuals by verifying the recombination
-operation and the integrity of the offspring chromosomes.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                              
-
                                            • setup_parents (fixture) – The fixture providing the parent individuals.
                                              • 
-
                                            • setup_problem (fixture) – The fixture providing the mock problem instance.
                                              • 
-
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_chichinadze_function module
                                            
-
                                            
-
                                            
-src.tests.test_chichinadze_function.setup_chichinadze()[source]
                                            
-
                                            Fixture for creating an instance of the Chichinadze problem.
                                            
-
                                            
-
                                            Returns:
                                            
-
                                            An instance of the Chichinadze problem.
                                            
-
                                            
-
                                            Return type:
                                            
-
                                            Chichinadze
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-src.tests.test_chichinadze_function.test_chichinadze_function(setup_chichinadze)[source]
                                            
-
                                            Test the Chichinadze function implementation.
                                            
-
                                            This test checks the calculation of the Chichinadze function value for given lists of float variables.
-It uses predefined inputs and compares the outputs to the expected values.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                            setup_chichinadze (fixture) – The fixture providing the Chichinadze problem instance.
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_compact_13 module
                                            
-
                                            
-
                                            
-src.tests.test_compact_13.test_compact_13()[source]
                                            
-
                                            Test the Compact13 class for calculating neighbor positions in a grid.
                                            
-
                                            This test verifies that the Compact13 class correctly calculates the positions
-of the 12 neighbors surrounding a given position in a grid. It ensures that:
-1. The number of neighbors is always 12 for positions not on the boundary of the grid.
-2. Positions on the boundary of the grid still correctly return 12 neighbors.
                                            
-
                                            The test uses three different positions within the grid to validate the functionality
-of the Compact13 neighborhood calculation.
                                            
-
                                            Notes
                                            
-
                                            The Compact13 neighborhood considers all 12 surrounding cells in a 5x5 grid centered
-on a given position, excluding the cell itself. The grid is assumed to be toroidal,
-meaning that positions on the edges wrap around to the opposite edge.
                                            
-
                                            The following assertions are made:
-- The number of calculated neighbor positions is 12 for various positions in the grid.
                                            
-
                                            
-
                                            Raises:
                                            
-
                                            AssertionError – If the number of calculated neighbor positions is not 12.
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_compact_21 module
                                            
-
                                            
-
                                            
-src.tests.test_compact_21.test_compact_21()[source]
                                            
-
                                            Test the Compact21 class for calculating neighbor positions in a grid.
                                            
-
                                            This test verifies that the Compact21 class correctly calculates the positions
-of the 20 neighbors surrounding a given position in a grid. It ensures that:
-1. The number of neighbors is always 20 for positions not on the boundary of the grid.
-2. Positions on the boundary of the grid still correctly return 20 neighbors.
                                            
-
                                            The test uses three different positions within the grid to validate the functionality
-of the Compact21 neighborhood calculation.
                                            
-
                                            Notes
                                            
-
                                            The Compact21 neighborhood considers all 20 surrounding cells in a 5x5 grid centered
-on a given position, excluding the cell itself. The grid is assumed to be toroidal,
-meaning that positions on the edges wrap around to the opposite edge.
                                            
-
                                            The following assertions are made:
-- The number of calculated neighbor positions is 20 for various positions in the grid.
                                            
-
                                            
-
                                            Raises:
                                            
-
                                            AssertionError – If the number of calculated neighbor positions is not 20.
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_compact_25 module
                                            
-
                                            
-
                                            
-src.tests.test_compact_25.test_compact_25()[source]
                                            
-
                                            Test the Compact25 class for calculating neighbor positions in a grid.
                                            
-
                                            This test verifies that the Compact25 class correctly calculates the positions
-of the 24 neighbors surrounding a given position in a grid. It ensures that:
-1. The number of neighbors is always 24 for positions not on the boundary of the grid.
-2. Positions on the boundary of the grid still correctly return 24 neighbors.
                                            
-
                                            The test uses three different positions within the grid to validate the functionality
-of the Compact25 neighborhood calculation.
                                            
-
                                            Notes
                                            
-
                                            The Compact25 neighborhood considers all 24 surrounding cells in a 5x5 grid centered
-on a given position, excluding the cell itself. The grid is assumed to be toroidal,
-meaning that positions on the edges wrap around to the opposite edge.
                                            
-
                                            The following assertions are made:
-- The number of calculated neighbor positions is 24 for various positions in the grid.
                                            
-
                                            
-
                                            Raises:
                                            
-
                                            AssertionError – If the number of calculated neighbor positions is not 24.
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_compact_9 module
                                            
-
                                            
-
                                            
-src.tests.test_compact_9.test_compact_9()[source]
                                            
-
                                            Test the Compact9 class for calculating neighbor positions in a grid.
                                            
-
                                            This test verifies that the Compact9 class correctly calculates the positions
-of the 8 neighbors surrounding a given position in a grid. It ensures that:
-1. The number of neighbors is always 8 for positions not on the boundary of the grid.
-2. Positions on the boundary of the grid still correctly return 8 neighbors.
                                            
-
                                            The test uses three different positions within the grid to validate the functionality
-of the Compact9 neighborhood calculation.
                                            
-
                                            Notes
                                            
-
                                            The Compact9 neighborhood considers all 8 surrounding cells in a 3x3 grid centered
-on a given position. The grid is assumed to be toroidal, meaning that positions on the
-edges wrap around to the opposite edge.
                                            
-
                                            The following assertions are made:
-- The number of calculated neighbor positions is 8 for various positions in the grid.
                                            
-
                                            
-
                                            Raises:
                                            
-
                                            AssertionError – If the number of calculated neighbor positions is not 8.
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_count_sat module
                                            
-
                                            
-
                                            
-src.tests.test_count_sat.test_count_sat()[source]
                                            
-
                                            Test the CountSat function implementation.
                                            
-
                                            This test verifies the calculation of the CountSat function value for specific binary input values.
                                            
-
                                            The CountSat function evaluates the satisfaction of a set of binary clauses. This test ensures the
-function computes the correct results for specific test inputs, including cases where all variables
-are set to 1.
                                            
-
                                            Examples
                                            
-
                                            >>> test_count_sat()
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_dropwave_function module
                                            
-
                                            
-
                                            
-src.tests.test_dropwave_function.setup_dropwave()[source]
                                            
-
                                            Fixture for creating an instance of the Dropwave problem.
                                            
-
                                            
-
                                            Returns:
                                            
-
                                            An instance of the Dropwave problem.
                                            
-
                                            
-
                                            Return type:
                                            
-
                                            Dropwave
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-src.tests.test_dropwave_function.test_dropwave_function(setup_dropwave)[source]
                                            
-
                                            Test the Dropwave function implementation.
                                            
-
                                            This test checks the calculation of the Dropwave function value for given lists of float variables.
-It uses predefined inputs and compares the outputs to the expected values.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                            setup_dropwave (fixture) – The fixture providing the Dropwave problem instance.
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_ecc module
                                            
-
                                            
-
                                            
-src.tests.test_ecc.ecc_instance()[source]
                                            
-
                                            Fixture for creating an instance of the Ecc class.
                                            
-
                                            This fixture returns an instance of the Ecc class to be used in tests.
                                            
-
                                            
-
-
                                            
-
                                            
-src.tests.test_ecc.test_ecc(ecc_instance)[source]
                                            
-
                                            Test the ECC function implementation.
                                            
-
                                            This test checks the calculation of the ECC function value for a given list of binary variables.
-It uses predefined inputs and compares the outputs to expected values.
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_flat_crossover module
                                            
-
                                            
-
                                            
-class src.tests.test_flat_crossover.MockProblem[source]
                                            
-
                                            Bases: AbstractProblem
                                            
-
                                            A mock problem class for testing purposes.
                                            
-
                                            
-
                                            
-f(x: list) → float[source]
                                            
-
                                            A mock fitness function that simply sums the chromosome values.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                            x (list) – A list of float variables.
                                            
-
                                            
-
                                            Returns:
                                            
-
                                            The sum of the list values.
                                            
-
                                            
-
                                            Return type:
                                            
-
                                            float
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
-
                                            
-
                                            
-src.tests.test_flat_crossover.setup_parents()[source]
                                            
-
                                            Fixture for creating a pair of parent Individual instances.
                                            
-
                                            
-
                                            Returns:
                                            
-
                                            A list containing two parent individuals with predefined chromosomes and size.
                                            
-
                                            
-
                                            Return type:
                                            
-
                                            list
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-src.tests.test_flat_crossover.setup_problem()[source]
                                            
-
                                            Fixture for creating a mock problem instance.
                                            
-
                                            
-
                                            Returns:
                                            
-
                                            An instance of the mock problem.
                                            
-
                                            
-
                                            Return type:
                                            
-
                                            MockProblem
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-src.tests.test_flat_crossover.test_flat_crossover(setup_parents, setup_problem)[source]
                                            
-
                                            Test the FlatCrossover function implementation.
                                            
-
                                            This test checks the flat crossover on a pair of parent individuals by verifying the recombination
-operation and the integrity of the offspring chromosomes.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                              
-
                                            • setup_parents (fixture) – The fixture providing the parent individuals.
                                              • 
-
                                            • setup_problem (fixture) – The fixture providing the mock problem instance.
                                              • 
-
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_float_uniform_mutation module
                                            
-
                                            
-
                                            
-class src.tests.test_float_uniform_mutation.MockProblem[source]
                                            
-
                                            Bases: AbstractProblem
                                            
-
                                            A mock problem class for testing purposes.
                                            
-
                                            
-
                                            
-f(x: list) → float[source]
                                            
-
                                            A mock fitness function that simply sums the chromosome values.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                            x (list) – A list of float variables.
                                            
-
                                            
-
                                            Returns:
                                            
-
                                            The sum of the list values.
                                            
-
                                            
-
                                            Return type:
                                            
-
                                            float
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
-
                                            
-
                                            
-src.tests.test_float_uniform_mutation.setup_individual()[source]
                                            
-
                                            Fixture for creating a sample Individual instance.
                                            
-
                                            
-
                                            Returns:
                                            
-
                                            An individual instance with a predefined chromosome and size.
                                            
-
                                            
-
                                            Return type:
                                            
-
                                            Individual
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-src.tests.test_float_uniform_mutation.setup_problem()[source]
                                            
-
                                            Fixture for creating a mock problem instance.
                                            
-
                                            
-
                                            Returns:
                                            
-
                                            An instance of the mock problem.
                                            
-
                                            
-
                                            Return type:
                                            
-
                                            MockProblem
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-src.tests.test_float_uniform_mutation.test_float_uniform_mutation(setup_individual, setup_problem)[source]
                                            
-
                                            Test the FloatUniformMutation function implementation.
                                            
-
                                            This test checks the uniform mutation on an individual’s chromosome by verifying the mutation
-operation and the integrity of the chromosome.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                              
-
                                            • setup_individual (fixture) – The fixture providing the sample individual.
                                              • 
-
                                            • setup_problem (fixture) – The fixture providing the mock problem instance.
                                              • 
-
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_fms module
                                            
-
                                            
-
                                            
-src.tests.test_fms.fms_instance()[source]
                                            
-
                                            Fixture for creating an instance of the Fms class.
                                            
-
                                            This fixture returns an instance of the Fms class to be used in tests.
                                            
-
                                            
-
                                            Returns:
                                            
-
                                            An instance of the Fms class.
                                            
-
                                            
-
                                            Return type:
                                            
-
                                            Fms
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-src.tests.test_fms.test_fms(fms_instance)[source]
                                            
-
                                            Test the Fms function implementation.
                                            
-
                                            This test checks the calculation of the FMS function value for a given list of binary variables.
-It uses a predefined input and compares the output to the expected value.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                            fms_instance (Fms) – An instance of the Fms class.
                                            
-
                                            
-
                                            
-
                                            Notes
                                            
-
                                            The test uses a randomly generated sample input chromosome of length 192 and checks if the function output is a float
-and non-negative. Additional checks with known values can be added for more thorough testing.
                                            
-
                                            
-
                                            Assertions
                                            
-
                                              
-
                                            • The fitness value should be a float.
                                              • 
-
                                            • The fitness value should be non-negative, as it’s a sum of squares of differences.
                                              • 
-
                                            
-
                                            Examples
                                            
-
                                            >>> test_fms(fms_instance)
-
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_grid module
                                            
-
                                            
-
                                            
-src.tests.test_grid.test_grid()[source]
                                            
-
                                            Test the Grid class implementation.
                                            
-
                                            This test checks the functionality of the Grid class for creating a 2D grid.
-It verifies the type, length, and content of the generated grid.
                                            
-
                                            The test performs the following steps:
-1. Create an instance of the Grid class with a 5x5 grid.
-2. Generate the 2D grid using the make_2d_grid method.
-3. Verify that the result is a list.
-4. Verify that the length of the list is 25 (5x5).
-5. Verify that each element in the list is a tuple.
-6. Verify the first and last elements of the list.
                                            
-
                                            
-
                                            Raises:
                                            
-
                                            AssertionError – If the result does not match the expected values.
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_griewank_function module
                                            
-
                                            
-
                                            
-src.tests.test_griewank_function.setup_griewank()[source]
                                            
-
                                            Fixture for creating an instance of the Griewank problem.
                                            
-
                                            
-
                                            Returns:
                                            
-
                                            An instance of the Griewank problem.
                                            
-
                                            
-
                                            Return type:
                                            
-
                                            Griewank
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-src.tests.test_griewank_function.test_griewank_function(setup_griewank)[source]
                                            
-
                                            Test the Griewank function implementation.
                                            
-
                                            This test checks the calculation of the Griewank function value for given lists of float variables.
-It uses predefined inputs and compares the outputs to the expected values.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                            setup_griewank (fixture) – The fixture providing the Griewank problem instance.
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_holzman_function module
                                            
-
                                            
-
                                            
-src.tests.test_holzman_function.setup_holzman()[source]
                                            
-
                                            Fixture for creating an instance of the Holzman problem.
                                            
-
                                            
-
                                            Returns:
                                            
-
                                            An instance of the Holzman problem.
                                            
-
                                            
-
                                            Return type:
                                            
-
                                            Holzman
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-src.tests.test_holzman_function.test_holzman_function(setup_holzman)[source]
                                            
-
                                            Test the Holzman function implementation.
                                            
-
                                            This test checks the calculation of the Holzman function value for given lists of float variables.
-It uses predefined inputs and compares the outputs to the expected values.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                            setup_holzman (fixture) – The fixture providing the Holzman problem instance.
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_individual module
                                            
-
                                            
-
                                            
-src.tests.test_individual.setup_individual()[source]
                                            
-
                                            Fixture to provide an instance of the Individual class with different configurations.
                                            
-
                                            
-
-
                                            
-
                                            
-src.tests.test_individual.test_get_set_neighbors()[source]
                                            
-
                                            Test getting and setting the list of neighbors for the individual.
                                            
-
                                            
-
-
                                            
-
                                            
-src.tests.test_individual.test_get_set_neighbors_positions()[source]
                                            
-
                                            Test getting and setting the positions of the individual’s neighbors.
                                            
-
                                            
-
-
                                            
-
                                            
-src.tests.test_individual.test_illegal_genome_type()[source]
                                            
-
                                            Test that an exception is raised when an illegal genome type is provided.
                                            
-
                                            
-
-
                                            
-
                                            
-src.tests.test_individual.test_individual_init()[source]
                                            
-
                                            Test the initialization of the Individual class.
                                            
-
                                            
-
-
                                            
-
                                            
-src.tests.test_individual.test_randomize_binary()[source]
                                            
-
                                            Test the randomization of the chromosome for a binary genome type.
                                            
-
                                            
-
-
                                            
-
                                            
-src.tests.test_individual.test_randomize_permutation()[source]
                                            
-
                                            Test the randomization of the chromosome for a permutation genome type.
                                            
-
                                            
-
-
                                            
-
                                            
-src.tests.test_individual.test_randomize_real_valued()[source]
                                            
-
                                            Test the randomization of the chromosome for a real-valued genome type.
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_insertion_mutation module
                                            
-
                                            
-
                                            
-src.tests.test_insertion_mutation.test_insertion_mutation()[source]
                                            
-
                                            Test the InsertionMutation class for the Individual class on the TSP problem.
                                            
-
                                            This test verifies that the InsertionMutation correctly mutates the chromosome of
-an Individual by inserting elements and ensures that the mutation is applied
-correctly. The test checks that:
-1. The chromosome is mutated.
-2. The chromosome size remains unchanged.
                                            
-
                                            The function uses random permutation for the chromosome initialization.
                                            
-
                                            Notes
                                            
-
                                            The test assumes that the InsertionMutation class correctly implements insertion
-mutation and that the TSP problem correctly evaluates the fitness of an individual.
                                            
-
                                            The following assertions are made:
-- At least one element in the chromosome is changed.
-- The size of the mutated individual’s chromosome matches the original size.
                                            
-
                                            
-
                                            Raises:
                                            
-
                                            AssertionError – If any of the conditions for correctness are not met.
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_levy_function module
                                            
-
                                            
-
                                            
-src.tests.test_levy_function.setup_levy()[source]
                                            
-
                                            Fixture to provide an instance of the Levy problem.
                                            
-
                                            
-
-
                                            
-
                                            
-src.tests.test_levy_function.test_levy_function(setup_levy)[source]
                                            
-
                                            Test the Levy function implementation.
                                            
-
                                            This test checks the calculation of the Levy function value for given lists of float variables.
-It uses predefined inputs and compares the outputs to the expected values.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                            setup_levy (fixture) – The fixture providing the Levy problem instance.
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_linear_5 module
                                            
-
                                            
-
                                            
-src.tests.test_linear_5.test_linear_5()[source]
                                            
-
                                            Test the Linear5 class for calculating neighbor positions in a grid.
                                            
-
                                            This test verifies that the Linear5 class correctly calculates the positions
-of the 4 neighbors surrounding a given position in a grid. It ensures that:
-1. The number of neighbors is always 4, which should include the positions
                                            
-
                                            
-
                                            directly adjacent in the linear neighborhood.
                                            
-
                                            
-
                                            The test uses three different positions within the grid to validate the functionality
-of the Linear5 neighborhood calculation.
                                            
-
                                            Notes
                                            
-
                                            The Linear5 neighborhood considers 4 neighboring cells aligned in a linear fashion
-(left, right, up, and down) in a 5x5 grid centered on a given position.
                                            
-
                                            The following assertions are made:
-- The number of calculated neighbor positions is 4 for various positions in the grid.
                                            
-
                                            
-
                                            Raises:
                                            
-
                                            AssertionError – If the number of calculated neighbor positions is not 4.
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_linear_9 module
                                            
-
                                            
-
                                            
-src.tests.test_linear_9.test_linear_9()[source]
                                            
-
                                            Test the Linear9 class for calculating neighbor positions in a grid.
                                            
-
                                            This test verifies that the Linear9 class correctly calculates the positions
-of the 8 neighbors surrounding a given position in a grid. It ensures that:
-1. The number of neighbors is always 8, which should include the positions
                                            
-
                                            
-
                                            surrounding the given position in the linear neighborhood.
                                            
-
                                            
-
                                            The test uses three different positions within the grid to validate the functionality
-of the Linear9 neighborhood calculation.
                                            
-
                                            Notes
                                            
-
                                            The Linear9 neighborhood considers 8 neighboring cells aligned in a linear fashion
-(left, right, up, down, and the diagonals) in a 5x5 grid centered on a given position.
                                            
-
                                            The following assertions are made:
-- The number of calculated neighbor positions is 8 for various positions in the grid.
                                            
-
                                            
-
                                            Raises:
                                            
-
                                            AssertionError – If the number of calculated neighbor positions is not 8.
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_linear_crossover module
                                            
-
                                            
-
                                            
-class src.tests.test_linear_crossover.MockProblem[source]
                                            
-
                                            Bases: AbstractProblem
                                            
-
                                            A mock problem class for testing purposes.
                                            
-
                                            
-
                                            
-f(x: list) → float[source]
                                            
-
                                            A mock fitness function that simply sums the chromosome values.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                            x (list) – A list of float variables.
                                            
-
                                            
-
                                            Returns:
                                            
-
                                            The sum of the list values.
                                            
-
                                            
-
                                            Return type:
                                            
-
                                            float
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
-
                                            
-
                                            
-src.tests.test_linear_crossover.setup_parents()[source]
                                            
-
                                            Fixture for creating a pair of parent Individual instances.
                                            
-
                                            
-
                                            Returns:
                                            
-
                                            A list containing two parent individuals with predefined chromosomes and size.
                                            
-
                                            
-
                                            Return type:
                                            
-
                                            list
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-src.tests.test_linear_crossover.setup_problem()[source]
                                            
-
                                            Fixture for creating a mock problem instance.
                                            
-
                                            
-
                                            Returns:
                                            
-
                                            An instance of the mock problem.
                                            
-
                                            
-
                                            Return type:
                                            
-
                                            MockProblem
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-src.tests.test_linear_crossover.test_linear_crossover(setup_parents, setup_problem)[source]
                                            
-
                                            Test the LinearCrossover function implementation.
                                            
-
                                            This test checks the linear crossover on a pair of parent individuals by verifying the recombination
-operation and the integrity of the offspring chromosomes.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                              
-
                                            • setup_parents (fixture) – The fixture providing the parent individuals.
                                              • 
-
                                            • setup_problem (fixture) – The fixture providing the mock problem instance.
                                              • 
-
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_matyas_function module
                                            
-
                                            
-
                                            
-src.tests.test_matyas_function.setup_matyas()[source]
                                            
-
                                            Fixture to provide an instance of the Matyas problem.
                                            
-
                                            
-
-
                                            
-
                                            
-src.tests.test_matyas_function.test_matyas_function(setup_matyas)[source]
                                            
-
                                            Test the Matyas function implementation.
                                            
-
                                            This test checks the calculation of the Matyas function value for given lists of float variables.
-It uses predefined inputs and compares the outputs to the expected values.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                            setup_matyas (fixture) – The fixture providing the Matyas problem instance.
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_maxcut100 module
                                            
-
                                            
-
                                            
-src.tests.test_maxcut100.maxcut_instance()[source]
                                            
-
                                            Fixture for creating an instance of the Maxcut100 class.
                                            
-
                                            This fixture returns an instance of the Maxcut100 class to be used in tests.
                                            
-
                                            
-
-
                                            
-
                                            
-src.tests.test_maxcut100.test_maxcut100()[source]
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_maxcut20_01 module
                                            
-
                                            
-
                                            
-src.tests.test_maxcut20_01.maxcut_instance()[source]
                                            
-
                                            Fixture for creating an instance of the Maxcut20_01 class.
                                            
-
                                            This fixture returns an instance of the Maxcut20_01 class to be used in tests.
                                            
-
                                            
-
-
                                            
-
                                            
-src.tests.test_maxcut20_01.test_maxcut20_01(maxcut_instance)[source]
                                            
-
                                            Test the MAXCUT function implementation.
                                            
-
                                            This test checks the calculation of the MAXCUT function value for a given list of binary variables.
-It uses predefined inputs and compares the outputs to expected values.
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_maxcut20_09 module
                                            
-
                                            
-
                                            
-src.tests.test_maxcut20_09.maxcut_instance()[source]
                                            
-
                                            Fixture for creating an instance of the Maxcut20_09 class.
                                            
-
                                            This fixture returns an instance of the Maxcut20_09 class to be used in tests.
                                            
-
                                            
-
-
                                            
-
                                            
-src.tests.test_maxcut20_09.test_maxcut20_09(maxcut_instance)[source]
                                            
-
                                            Test the MAXCUT function implementation.
                                            
-
                                            This test checks the calculation of the MAXCUT function value for a given list of binary variables.
-It uses predefined inputs and compares the outputs to expected values.
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_mmdp module
                                            
-
                                            
-
                                            
-src.tests.test_mmdp.mmdp_instance()[source]
                                            
-
                                            Fixture for creating an instance of the Mmdp class.
                                            
-
                                            This fixture returns an instance of the Mmdp class to be used in tests.
                                            
-
                                            
-
-
                                            
-
                                            
-src.tests.test_mmdp.test_mmdp_function(mmdp_instance)[source]
                                            
-
                                            Test the Mmdp function implementation.
                                            
-
                                            This test checks the calculation of the MMDP fitness value for given lists of binary variables.
-It uses predefined inputs and compares the outputs to the expected values.
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_one_max module
                                            
-
                                            
-
                                            
-src.tests.test_one_max.test_one_max()[source]
                                            
-
                                            Test the OneMax function implementation.
                                            
-
                                            This test verifies the calculation of the OneMax function value for specific binary input values.
                                            
-
                                            The OneMax function evaluates the number of 1s in a binary list. This test ensures that the function
-computes the correct number of 1s for various test inputs.
                                            
-
                                            Examples
                                            
-
                                            >>> test_one_max()
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_one_point_crossover module
                                            
-
                                            
-
                                            
-src.tests.test_one_point_crossover.test_one_point_crossover()[source]
                                            
-
                                            Test the OnePointCrossover class for generating offspring from two parents.
                                            
-
                                            This test verifies that the OnePointCrossover class correctly performs one-point crossover
-between two parent individuals. It ensures that:
-1. The offspring generated have the same chromosome size as the parents.
-2. The chromosomes of the offspring contain only valid binary values (0 or 1).
                                            
-
                                            The test performs the following checks:
-- Both offspring have the same chromosome size.
-- Each chromosome in the offspring is composed of binary values (0 or 1).
                                            
-
                                            
-
                                            Raises:
                                            
-
                                            AssertionError – If any of the conditions for the crossover operation are not met.
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_optimizer_alpha_cga module
                                            
-
                                            
-
                                            
-class src.tests.test_optimizer_alpha_cga.BinaryProblem[source]
                                            
-
                                            Bases: object
                                            
-
                                            Example problem class to be maximized for binary chromosomes.
                                            
-
                                            This class implements the OneMax problem where the goal is to maximize the number of 1s in a binary string.
                                            
-
                                            
-
                                            
-__init__()[source]
                                            
-
                                            
-
-
                                            
-
                                            
-f(x)[source]
                                            
-
                                            Compute the objective function value.
                                            
-
                                            This method counts the number of 1s in the binary chromosome.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                            x (list or numpy.ndarray) – The input chromosome represented as a list or array of binary values (0s and 1s).
                                            
-
                                            
-
                                            Returns:
                                            
-
                                            The computed value, which is the count of 1s in the chromosome.
                                            
-
                                            
-
                                            Return type:
                                            
-
                                            int
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
-
                                            
-
                                            
-class src.tests.test_optimizer_alpha_cga.PermutationProblem(target: List[int])[source]
                                            
-
                                            Bases: object
                                            
-
                                            Example problem class to be minimized using a permutation-based approach.
                                            
-
                                            This class implements a simple objective function that measures the sum of absolute differences
-between the chromosome and a target permutation.
                                            
-
                                            
-
                                            
-__init__(target: List[int])[source]
                                            
-
                                            Initialize the PermutationProblem with a target permutation.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                            target (list of int) – The target permutation that the algorithm aims to find.
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-f(x: List[int]) → float[source]
                                            
-
                                            Compute the objective function value.
                                            
-
                                            This method implements the sum of absolute differences function.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                            x (list) – The input chromosome represented as a list of integers (permutation).
                                            
-
                                            
-
                                            Returns:
                                            
-
                                            The computed value of the function given the input x.
                                            
-
                                            
-
                                            Return type:
                                            
-
                                            float
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-test_optimizer_alpha_cga_permutation()[source]
                                            
-
                                            Test alpha_cga on a permutation-based problem where the target is the identity permutation.
                                            
-
                                            
-
-
                                            
-
-
                                            
-
                                            
-class src.tests.test_optimizer_alpha_cga.RealProblem[source]
                                            
-
                                            Bases: object
                                            
-
                                            Example problem class to be minimized.
                                            
-
                                            This class implements a simple sum of squares function with a global minimum value of 0,
-achieved when all elements of the chromosome are equal to 0.
                                            
-
                                            
-
                                            
-__init__()[source]
                                            
-
                                            
-
-
                                            
-
                                            
-f(x)[source]
                                            
-
                                            Compute the objective function value.
                                            
-
                                            This method implements the sum of squares function.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                            x (list or numpy.ndarray) – The input chromosome represented as a list or array of real values.
                                            
-
                                            
-
                                            Returns:
                                            
-
                                            The computed value of the function given the input x.
                                            
-
                                            
-
                                            Return type:
                                            
-
                                            float
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
-
                                            
-
                                            
-src.tests.test_optimizer_alpha_cga.test_optimizer_alpha_cga_binary()[source]
                                            
-
                                            Test alpha_cga on a binary OneMax problem.
                                            
-
                                            
-
-
                                            
-
                                            
-src.tests.test_optimizer_alpha_cga.test_optimizer_alpha_cga_no_variation()[source]
                                            
-
                                            Test alpha_cga with no crossover or mutation.
                                            
-
                                            
-
-
                                            
-
                                            
-src.tests.test_optimizer_alpha_cga.test_optimizer_alpha_cga_real()[source]
                                            
-
                                            Test alpha_cga on a real-valued sum of squares problem.
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_optimizer_ccga module
                                            
-
                                            
-
                                            
-class src.tests.test_optimizer_ccga.BinaryProblem[source]
                                            
-
                                            Bases: object
                                            
-
                                            Example problem class to be maximized for binary chromosomes.
                                            
-
                                            This class implements the OneMax problem where the goal is to maximize the number of 1s in a binary string.
                                            
-
                                            
-
                                            
-__init__()[source]
                                            
-
                                            
-
-
                                            
-
                                            
-f(x)[source]
                                            
-
                                            Compute the objective function value.
                                            
-
                                            This method counts the number of 1s in the binary chromosome.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                            x (list or numpy.ndarray) – The input chromosome represented as a list or array of binary values (0s and 1s).
                                            
-
                                            
-
                                            Returns:
                                            
-
                                            The computed value, which is the count of 1s in the chromosome.
                                            
-
                                            
-
                                            Return type:
                                            
-
                                            int
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
-
                                            
-
                                            
-src.tests.test_optimizer_ccga.test_optimizer_ccga_binary()[source]
                                            
-
                                            Test ccga on a binary OneMax problem.
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_optimizer_cga module
                                            
-
                                            
-
                                            
-class src.tests.test_optimizer_cga.BinaryProblem[source]
                                            
-
                                            Bases: object
                                            
-
                                            Example problem class to be maximized for binary chromosomes.
                                            
-
                                            This class implements the OneMax problem where the goal is to maximize the number of 1s in a binary string.
                                            
-
                                            
-
                                            
-__init__()[source]
                                            
-
                                            
-
-
                                            
-
                                            
-f(x)[source]
                                            
-
                                            Compute the objective function value.
                                            
-
                                            This method counts the number of 1s in the binary chromosome.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                            x (list or numpy.ndarray) – The input chromosome represented as a list or array of binary values (0s and 1s).
                                            
-
                                            
-
                                            Returns:
                                            
-
                                            The computed value, which is the count of 1s in the chromosome.
                                            
-
                                            
-
                                            Return type:
                                            
-
                                            int
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
-
                                            
-
                                            
-class src.tests.test_optimizer_cga.PermutationProblem(target: List[int])[source]
                                            
-
                                            Bases: object
                                            
-
                                            Example problem class to be minimized using a permutation-based approach.
                                            
-
                                            This class implements a simple objective function that measures the sum of absolute differences
-between the chromosome and a target permutation.
                                            
-
                                            
-
                                            
-__init__(target: List[int])[source]
                                            
-
                                            Initialize the PermutationProblem with a target permutation.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                            target (list of int) – The target permutation that the algorithm aims to find.
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-f(x: List[int]) → float[source]
                                            
-
                                            Compute the objective function value.
                                            
-
                                            This method implements the sum of absolute differences function.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                            x (list) – The input chromosome represented as a list of integers (permutation).
                                            
-
                                            
-
                                            Returns:
                                            
-
                                            The computed value of the function given the input x.
                                            
-
                                            
-
                                            Return type:
                                            
-
                                            float
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-test_optimizer_cga_permutation()[source]
                                            
-
                                            Test CGA on a permutation-based problem where the target is the identity permutation.
                                            
-
                                            
-
-
                                            
-
-
                                            
-
                                            
-class src.tests.test_optimizer_cga.RealProblem[source]
                                            
-
                                            Bases: object
                                            
-
                                            Example problem class to be minimized.
                                            
-
                                            This class implements a simple sum of squares function with a global minimum value of 0,
-achieved when all elements of the chromosome are equal to 0.
                                            
-
                                            
-
                                            
-__init__()[source]
                                            
-
                                            
-
-
                                            
-
                                            
-f(x)[source]
                                            
-
                                            Compute the objective function value.
                                            
-
                                            This method implements the sum of squares function.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                            x (list or numpy.ndarray) – The input chromosome represented as a list or array of real values.
                                            
-
                                            
-
                                            Returns:
                                            
-
                                            The computed value of the function given the input x.
                                            
-
                                            
-
                                            Return type:
                                            
-
                                            float
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
-
                                            
-
                                            
-src.tests.test_optimizer_cga.test_optimizer_cga_binary()[source]
                                            
-
                                            Test CGA on a binary OneMax problem.
                                            
-
                                            
-
-
                                            
-
                                            
-src.tests.test_optimizer_cga.test_optimizer_cga_no_variation()[source]
                                            
-
                                            Test CGA with no crossover or mutation.
                                            
-
                                            
-
-
                                            
-
                                            
-src.tests.test_optimizer_cga.test_optimizer_cga_real()[source]
                                            
-
                                            Test CGA on a real-valued sum of squares problem.
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_optimizer_mccga module
                                            
-
                                            
-
                                            
-class src.tests.test_optimizer_mccga.RealProblem[source]
                                            
-
                                            Bases: object
                                            
-
                                            Example problem class to be minimized.
                                            
-
                                            This class implements a simple sum of squares function with a global minimum value of 0,
-achieved when all elements of the chromosome are equal to 0.
                                            
-
                                            
-
                                            
-__init__()[source]
                                            
-
                                            
-
-
                                            
-
                                            
-f(x)[source]
                                            
-
                                            Compute the objective function value.
                                            
-
                                            This method implements the sum of squares function.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                            x (list or numpy.ndarray) – The input chromosome represented as a list or array of real values.
                                            
-
                                            
-
                                            Returns:
                                            
-
                                            The computed value of the function given the input x.
                                            
-
                                            
-
                                            Return type:
                                            
-
                                            float
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
-
                                            
-
                                            
-src.tests.test_optimizer_mccga.test_optimizer_mcccga_binary()[source]
                                            
-
                                            Test mcccga on a binary OneMax problem.
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_optimizer_sync_cga module
                                            
-
                                            
-
                                            
-class src.tests.test_optimizer_sync_cga.BinaryProblem[source]
                                            
-
                                            Bases: object
                                            
-
                                            Example problem class to be maximized for binary chromosomes.
                                            
-
                                            This class implements the OneMax problem where the goal is to maximize the number of 1s in a binary string.
                                            
-
                                            
-
                                            
-__init__()[source]
                                            
-
                                            
-
-
                                            
-
                                            
-f(x)[source]
                                            
-
                                            Compute the objective function value.
                                            
-
                                            This method counts the number of 1s in the binary chromosome.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                            x (list or numpy.ndarray) – The input chromosome represented as a list or array of binary values (0s and 1s).
                                            
-
                                            
-
                                            Returns:
                                            
-
                                            The computed value, which is the count of 1s in the chromosome.
                                            
-
                                            
-
                                            Return type:
                                            
-
                                            int
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
-
                                            
-
                                            
-class src.tests.test_optimizer_sync_cga.PermutationProblem(target: List[int])[source]
                                            
-
                                            Bases: object
                                            
-
                                            Example problem class to be minimized using a permutation-based approach.
                                            
-
                                            This class implements a simple objective function that measures the sum of absolute differences
-between the chromosome and a target permutation.
                                            
-
                                            
-
                                            
-__init__(target: List[int])[source]
                                            
-
                                            Initialize the PermutationProblem with a target permutation.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                            target (list of int) – The target permutation that the algorithm aims to find.
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-f(x: List[int]) → float[source]
                                            
-
                                            Compute the objective function value.
                                            
-
                                            This method implements the sum of absolute differences function.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                            x (list) – The input chromosome represented as a list of integers (permutation).
                                            
-
                                            
-
                                            Returns:
                                            
-
                                            The computed value of the function given the input x.
                                            
-
                                            
-
                                            Return type:
                                            
-
                                            float
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-test_optimizer_sync_cga_permutation()[source]
                                            
-
                                            Test sync_cga on a permutation-based problem where the target is the identity permutation.
                                            
-
                                            
-
-
                                            
-
-
                                            
-
                                            
-class src.tests.test_optimizer_sync_cga.RealProblem[source]
                                            
-
                                            Bases: object
                                            
-
                                            Example problem class to be minimized.
                                            
-
                                            This class implements a simple sum of squares function with a global minimum value of 0,
-achieved when all elements of the chromosome are equal to 0.
                                            
-
                                            
-
                                            
-__init__()[source]
                                            
-
                                            
-
-
                                            
-
                                            
-f(x)[source]
                                            
-
                                            Compute the objective function value.
                                            
-
                                            This method implements the sum of squares function.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                            x (list or numpy.ndarray) – The input chromosome represented as a list or array of real values.
                                            
-
                                            
-
                                            Returns:
                                            
-
                                            The computed value of the function given the input x.
                                            
-
                                            
-
                                            Return type:
                                            
-
                                            float
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
-
                                            
-
                                            
-src.tests.test_optimizer_sync_cga.test_optimizer_sync_cga_binary()[source]
                                            
-
                                            Test sync_cga on a binary OneMax problem.
                                            
-
                                            
-
-
                                            
-
                                            
-src.tests.test_optimizer_sync_cga.test_optimizer_sync_cga_no_variation()[source]
                                            
-
                                            Test sync_cga with no crossover or mutation.
                                            
-
                                            
-
-
                                            
-
                                            
-src.tests.test_optimizer_sync_cga.test_optimizer_sync_cga_real()[source]
                                            
-
                                            Test sync_cga on a real-valued sum of squares problem.
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_peak module
                                            
-
                                            
-
                                            
-src.tests.test_peak.peak_instance()[source]
                                            
-
                                            Fixture for creating an instance of the Peak class.
                                            
-
                                            This fixture returns an instance of the Peak class to be used in tests.
                                            
-
                                            
-
-
                                            
-
                                            
-src.tests.test_peak.test_peak(peak_instance)[source]
                                            
-
                                            Test the Peak function implementation.
                                            
-
                                            This test checks the calculation of the Peak fitness value for given lists of binary variables.
-It uses predefined inputs and compares the outputs to the expected values.
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_pmx_crossover module
                                            
-
                                            
-
                                            
-src.tests.test_pmx_crossover.test_pmx_crossover()[source]
                                            
-
                                            Test the PMXCrossover class for generating offspring from two permutation parents.
                                            
-
                                            This test verifies that the PMXCrossover class correctly performs partially matched crossover
-(PMX) between two parent individuals. It ensures that:
-1. The offspring generated have the same chromosome size as the parents.
                                            
-
                                            The test performs the following checks:
-- Both offspring have the same chromosome size as the parents.
                                            
-
                                            
-
                                            Raises:
                                            
-
                                            AssertionError – If any of the conditions for the crossover operation are not met.
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_population module
                                            
-
                                            
-
                                            
-class src.tests.test_population.MockProblem[source]
                                            
-
                                            Bases: AbstractProblem
                                            
-
                                            A mock problem for testing purposes.
                                            
-
                                            
-
                                            
-f(chromosome: List[float]) → float[source]
                                            
-
                                            Returns the sum of the chromosome as the fitness value.
                                            
-
                                            
-
-
                                            
-
                                            
-f(chromosome: List[float]) → float[source]
                                            
-
                                            Evaluate the fitness of a given solution x.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                            x (list) – A list representing a candidate solution.
                                            
-
                                            
-
                                            Returns:
                                            
-
                                            The fitness value of the candidate solution.
                                            
-
                                            
-
                                            Return type:
                                            
-
                                            float
                                            
-
                                            
-
                                            Raises:
                                            
-
                                            NotImplementedError – If the method is not implemented by a subclass.
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
-
                                            
-
                                            
-src.tests.test_population.setup_population()[source]
                                            
-
                                            Fixture for setting up a Population object.
                                            
-
                                            
-
                                            Returns:
                                            
-
                                            A population instance with a mock problem.
                                            
-
                                            
-
                                            Return type:
                                            
-
                                            Population
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-src.tests.test_population.test_fitness_evaluation(setup_population)[source]
                                            
-
                                            Test the fitness evaluation of the individuals.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                              
-
                                            • setup_population (Population) – The population fixture.
                                              • 
-
                                            • Asserts
                                              • 
-
                                            • -------
                                              • 
-
                                            • performed. (True if the fitness evaluation is correctly)
                                              • 
-
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-src.tests.test_population.test_initial_population_size(setup_population)[source]
                                            
-
                                            Test the size of the initial population.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                              
-
                                            • setup_population (Population) – The population fixture.
                                              • 
-
                                            • Asserts
                                              • 
-
                                            • -------
                                              • 
-
                                            • correct. (True if the size of the population is)
                                              • 
-
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-src.tests.test_population.test_neighborhood_assignment(setup_population)[source]
                                            
-
                                            Test the neighborhood assignment for the individuals.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                              
-
                                            • setup_population (Population) – The population fixture.
                                              • 
-
                                            • Asserts
                                              • 
-
                                            • -------
                                              • 
-
                                            • assigned. (True if neighborhood positions are correctly)
                                              • 
-
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_pow_function module
                                            
-
                                            
-
                                            
-class src.tests.test_pow_function.Pow[source]
                                            
-
                                            Bases: AbstractProblem
                                            
-
                                            Pow function implementation for optimization problems.
                                            
-
                                            The Pow function is widely used for testing optimization algorithms.
-The function is usually evaluated on the hypercube x_i ∈ [-5.0, 15.0].
                                            
-
                                            
-
                                            
-None
                                            
-
                                            
-
-
                                            
-
                                            
-f(x: list) → float[source]
                                            
-
                                            Calculates the Pow function value for a given list of variables.
                                            
-
                                            
-
-
                                            Notes
                                            
-
                                            -5.0 ≤ xi ≤ 15.0
-Global minimum at f(5, 7, 9, 3, 2) = 0
                                            
-
                                            
-
                                            
-f(x: list) → float[source]
                                            
-
                                            Calculate the Pow function value for a given list of variables.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                            x (list) – A list of float variables.
                                            
-
                                            
-
                                            Returns:
                                            
-
                                            The Pow function value.
                                            
-
                                            
-
                                            Return type:
                                            
-
                                            float
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_powell_function module
                                            
-
                                            
-
                                            
-src.tests.test_powell_function.setup_powell()[source]
                                            
-
                                            Fixture to provide an instance of the Powell problem.
                                            
-
                                            
-
-
                                            
-
                                            
-src.tests.test_powell_function.test_powell_function(setup_powell)[source]
                                            
-
                                            Test the Powell function implementation.
                                            
-
                                            This test checks the calculation of the Powell function value for given lists of float variables.
-It uses predefined inputs and compares the outputs to the expected values.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                            setup_powell (fixture) – The fixture providing the Powell problem instance.
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_rastrigin module
                                            
-
                                            
-
                                            
-src.tests.test_rastrigin.test_rastrigin()[source]
                                            
-
                                            Test the Rastrigin function implementation.
                                            
-
                                            This test verifies the calculation of the Rastrigin function value for a given list of continuous variables.
-It compares the function output with known expected values.
                                            
-
                                            Notes
                                            
-
                                            The Rastrigin function is a well-known benchmark function used in optimization problems.
-It has a global minimum value of 0, which is achieved when all variables are 0.
                                            
-
                                            
-
                                            Assertions
                                            
-
                                              
-
                                            • The function should return the correct values for predefined inputs.
                                              • 
-
                                            • The function should return 0 for an input list of all zeros.
                                              • 
-
                                            
-
                                            Examples
                                            
-
                                            >>> test_rastrigin()
-
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_rosenbrock module
                                            
-
                                            
-
                                            
-src.tests.test_rosenbrock.test_rosenbrock()[source]
                                            
-
                                            Test the Rosenbrock function implementation.
                                            
-
                                            This test verifies the calculation of the Rosenbrock function value for a given list of continuous variables.
-It compares the function output with known expected values.
                                            
-
                                            Notes
                                            
-
                                            The Rosenbrock function, also known as the Rosenbrock’s valley or Rosenbrock’s banana function,
-is a common test problem for optimization algorithms. The global minimum is at (1, …, 1), where the function value is 0.
                                            
-
                                            
-
                                            Assertions
                                            
-
                                              
-
                                            • The function should return the correct values for predefined inputs.
                                              • 
-
                                            • The function should return 0 for an input list of all ones.
                                              • 
-
                                            
-
                                            Examples
                                            
-
                                            >>> test_rosenbrock()
-
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_rothellipsoid_function module
                                            
-
                                            
-
                                            
-src.tests.test_rothellipsoid_function.setup_rothellipsoid()[source]
                                            
-
                                            Fixture to provide an instance of the Rotated Hyper-Ellipsoid problem.
                                            
-
                                            
-
-
                                            
-
                                            
-src.tests.test_rothellipsoid_function.test_rothellipsoid_function(setup_rothellipsoid)[source]
                                            
-
                                            Test the Rotated Hyper-Ellipsoid function implementation.
                                            
-
                                            This test checks the calculation of the Rotated Hyper-Ellipsoid function value for given lists of float variables.
-It uses predefined inputs and compares the outputs to the expected values.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                            setup_rothellipsoid (fixture) – The fixture providing the Rotated Hyper-Ellipsoid problem instance.
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_roulette_wheel_selection module
                                            
-
                                            
-
                                            
-src.tests.test_roulette_wheel_selection.test_roulette_wheel_selection()[source]
                                            
-
                                            Test the RouletteWheelSelection class implementation.
                                            
-
                                            This test verifies the functionality of the RouletteWheelSelection for selecting parent individuals
-from a population. It ensures that the selected parents have valid attributes and different chromosomes
-and positions.
                                            
-
                                            The test performs the following checks:
-1. Each selected parent has the correct chromosome size and a non-None fitness value.
-2. Each selected parent has valid types for neighbors_positions and position attributes.
-3. The selected parents have different chromosomes and positions.
                                            
-
                                            
-
                                            Raises:
                                            
-
                                            AssertionError – If any of the conditions for the parent selection are not met.
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_schaffer2_function module
                                            
-
                                            
-
                                            
-src.tests.test_schaffer2_function.setup_schaffer2()[source]
                                            
-
                                            Fixture to provide an instance of the Schaffer2 problem.
                                            
-
                                            
-
-
                                            
-
                                            
-src.tests.test_schaffer2_function.test_schaffer2_function(setup_schaffer2)[source]
                                            
-
                                            Test the Modified Schaffer function #2 implementation.
                                            
-
                                            This test checks the calculation of the Modified Schaffer function #2 value
-for given lists of float variables. It uses predefined inputs and compares
-the outputs to the expected values.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                            setup_schaffer2 (fixture) – The fixture providing the Schaffer2 problem instance.
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_schaffer_function module
                                            
-
                                            
-
                                            
-src.tests.test_schaffer_function.setup_schaffer()[source]
                                            
-
                                            Fixture to provide an instance of the Schaffer problem.
                                            
-
                                            
-
-
                                            
-
                                            
-src.tests.test_schaffer_function.test_schaffer_function(setup_schaffer)[source]
                                            
-
                                            Test the Modified Schaffer function #1 implementation.
                                            
-
                                            This test checks the calculation of the Modified Schaffer function #1 value for given lists of float variables.
-It uses predefined inputs and compares the outputs to the expected values.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                            setup_schaffer (fixture) – The fixture providing the Schaffer problem instance.
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_schwefel module
                                            
-
                                            
-
                                            
-src.tests.test_schwefel.test_schwefel()[source]
                                            
-
                                            Test the Schwefel function implementation.
                                            
-
                                            This test verifies the functionality of the Schwefel function on a set of predefined input values.
                                            
-
                                            The Schwefel function is used as a benchmark in optimization problems, and this test ensures
-that the function computes the expected results for given inputs.
                                            
-
                                            It performs assertions to check:
-- The correctness of the function’s output for specific input values.
-- The proper rounding of the function’s output to three decimal places.
                                            
-
                                            Notes
                                            
-
                                            The Schwefel function is typically used to evaluate optimization algorithms, and the expected values
-for the test cases are specific to the known behavior of the function.
                                            
-
                                            Examples
                                            
-
                                            >>> test_schwefel()
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_shuffle_mutation module
                                            
-
                                            
-
                                            
-src.tests.test_shuffle_mutation.test_shuffle_mutation()[source]
                                            
-
                                            Test the ShuffleMutation class for the Individual class on the TSP problem.
                                            
-
                                            This test verifies that the ShuffleMutation correctly mutates the chromosome of
-an Individual by shuffling the elements. It ensures that:
-1. The chromosome is mutated.
-2. The chromosome size remains unchanged.
                                            
-
                                            The function initializes the chromosome with a random permutation of integers
-from 1 to CHSIZE and applies the shuffle mutation.
                                            
-
                                            Notes
                                            
-
                                            The test assumes that the ShuffleMutation class correctly implements shuffle
-mutation and that the TSP problem correctly evaluates the fitness of an individual.
                                            
-
                                            The following assertions are made:
-- At least one element in the chromosome is changed.
-- The size of the mutated individual’s chromosome matches the original size.
                                            
-
                                            
-
                                            Raises:
                                            
-
                                            AssertionError – If any of the conditions for correctness are not met.
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_sphere module
                                            
-
                                            
-
                                            
-src.tests.test_sphere.test_sphere()[source]
                                            
-
                                            Test the Sphere function implementation.
                                            
-
                                            This test checks the calculation of the Sphere function value for given input values.
                                            
-
                                            The Sphere function is a common benchmark function in optimization, where the global minimum is
-at f(0, 0, …, 0) = 0. This test ensures that the function computes the correct results for specific
-input values and verifies that the function behaves as expected.
                                            
-
                                            It performs assertions to validate:
-- The correctness of the function’s output for specific test inputs.
-- The rounding of the function’s output to three decimal places.
                                            
-
                                            Examples
                                            
-
                                            >>> test_sphere()
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_styblinskitang_function module
                                            
-
                                            
-
                                            
-src.tests.test_styblinskitang_function.setup_styblinski_tang()[source]
                                            
-
                                            Fixture to provide an instance of the StyblinskiTang problem.
                                            
-
                                            
-
-
                                            
-
                                            
-src.tests.test_styblinskitang_function.test_styblinskitang_function(setup_styblinski_tang)[source]
                                            
-
                                            Test the Styblinski-Tang function implementation.
                                            
-
                                            This test checks the calculation of the Styblinski-Tang function value
-for given lists of float variables. It uses predefined inputs and compares
-the outputs to the expected values.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                            setup_styblinski_tang (fixture) – The fixture providing the StyblinskiTang problem instance.
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_sumofdifferentpowers_function module
                                            
-
                                            
-
                                            
-src.tests.test_sumofdifferentpowers_function.setup_sumofdifferentpowers()[source]
                                            
-
                                            Fixture to create an instance of the Sumofdifferentpowers problem.
                                            
-
                                            
-
-
                                            
-
                                            
-src.tests.test_sumofdifferentpowers_function.test_sumofdifferentpowers_function(setup_sumofdifferentpowers)[source]
                                            
-
                                            Test the Sum of Different Powers function implementation.
                                            
-
                                            This test checks the calculation of the Sum of Different Powers function value
-for given lists of float variables. It uses predefined inputs and compares
-the outputs to the expected values.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                            setup_sumofdifferentpowers (fixture) – The fixture providing the Sumofdifferentpowers problem instance.
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_swap_mutation module
                                            
-
                                            
-
                                            
-src.tests.test_swap_mutation.test_swap_mutation()[source]
                                            
-
                                            Test the SwapMutation class for the Individual class on the TSP problem.
                                            
-
                                            This test verifies that the SwapMutation correctly mutates the chromosome of
-an Individual by swapping two elements. It ensures that:
-1. The chromosome is mutated.
-2. The chromosome size remains unchanged.
                                            
-
                                            The function initializes the chromosome with a random permutation of integers
-from 1 to CHSIZE and applies the swap mutation.
                                            
-
                                            Notes
                                            
-
                                            The test assumes that the SwapMutation class correctly implements swap mutation
-and that the TSP problem correctly evaluates the fitness of an individual.
                                            
-
                                            The following assertions are made:
-- At least one element in the chromosome is changed.
-- The size of the mutated individual’s chromosome matches the original size.
                                            
-
                                            
-
                                            Raises:
                                            
-
                                            AssertionError – If any of the conditions for correctness are not met.
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_threehumps_function module
                                            
-
                                            
-
                                            
-src.tests.test_threehumps_function.setup_threehumps()[source]
                                            
-
                                            Fixture to provide the Threehumps problem instance.
                                            
-
                                            
-
-
                                            
-
                                            
-src.tests.test_threehumps_function.test_threehumps_function(setup_threehumps)[source]
                                            
-
                                            Test the Three Hump Camel function implementation.
                                            
-
                                            This test checks the calculation of the Three Hump Camel function value
-for given lists of float variables. It uses predefined inputs and compares
-the outputs to the expected values.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                            setup_threehumps (fixture) – The fixture providing the Threehumps problem instance.
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_tournament_selection module
                                            
-
                                            
-
                                            
-src.tests.test_tournament_selection.test_tournament_selection()[source]
                                            
-
                                            Test the TournamentSelection class implementation.
                                            
-
                                            This test verifies the functionality of the TournamentSelection for selecting parent individuals
-from a population. It ensures that the selected parents have valid attributes and different chromosomes
-and positions.
                                            
-
                                            The test performs the following checks:
-1. Each selected parent has the correct chromosome size and a non-None fitness value.
-2. Each selected parent has valid types for neighbors_positions and position attributes.
-3. The selected parents have different chromosomes and positions.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                            None
                                            
-
                                            
-
                                            Raises:
                                            
-
                                            AssertionError – If any of the conditions for the parent selection are not met.
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_tsp module
                                            
-
                                            
-
                                            
-src.tests.test_tsp.test_tsp()[source]
                                            
-
                                            Test the Tsp function implementation.
                                            
-
                                            This test verifies the calculation of the TSP (Traveling Salesman Problem) function value for
-different permutations of cities.
                                            
-
                                            The TSP function evaluates the total distance for a given permutation of cities. This test checks
-if the function computes the correct distance for specific permutations generated using different
-random seeds.
                                            
-
                                            Examples
                                            
-
                                            >>> test_tsp()
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_two_opt_mutation module
                                            
-
                                            
-
                                            
-src.tests.test_two_opt_mutation.test_two_opt_mutation()[source]
                                            
-
                                            Test the TwoOptMutation class for the Individual class on the TSP problem.
                                            
-
                                            This test verifies that the TwoOptMutation correctly mutates the chromosome of
-an Individual by applying the 2-opt mutation technique. It ensures that:
-1. The chromosome is mutated by swapping two edges in the permutation.
-2. The chromosome size remains unchanged.
                                            
-
                                            The function initializes the chromosome with a random permutation of integers
-from 1 to CHSIZE and applies the two-opt mutation.
                                            
-
                                            Notes
                                            
-
                                            The test assumes that the TwoOptMutation class correctly implements the 2-opt mutation
-and that the TSP problem correctly evaluates the fitness of an individual.
                                            
-
                                            The following assertions are made:
-- At least one element in the chromosome is changed.
-- The size of the mutated individual’s chromosome matches the original size.
                                            
-
                                            
-
                                            Raises:
                                            
-
                                            AssertionError – If any of the conditions for correctness are not met.
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_two_point_crossover module
                                            
-
                                            
-
                                            
-src.tests.test_two_point_crossover.test_two_point_crossover()[source]
                                            
-
                                            Test the TwoPointCrossover class implementation.
                                            
-
                                            This test verifies the functionality of the TwoPointCrossover class for binary chromosomes.
-It ensures that the crossover operation produces valid offspring with the expected chromosome size.
                                            
-
                                            The test performs the following checks:
-1. Both offspring have the same chromosome size as the parents.
-2. The chromosomes of the offspring contain only valid binary values (0 or 1).
                                            
-
                                            
-
                                            Raises:
                                            
-
                                            AssertionError – If any of the conditions for the crossover operation are not met.
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_unfair_average_crossover module
                                            
-
                                            
-
                                            
-class src.tests.test_unfair_average_crossover.MockProblem[source]
                                            
-
                                            Bases: AbstractProblem
                                            
-
                                            A mock problem class for testing purposes.
                                            
-
                                            
-
                                            
-f(x: list) → float[source]
                                            
-
                                            A mock fitness function that simply sums the chromosome values.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                            x (list) – A list of float variables.
                                            
-
                                            
-
                                            Returns:
                                            
-
                                            The sum of the list values.
                                            
-
                                            
-
                                            Return type:
                                            
-
                                            float
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
-
                                            
-
                                            
-src.tests.test_unfair_average_crossover.setup_parents()[source]
                                            
-
                                            Fixture for creating a pair of parent Individual instances.
                                            
-
                                            
-
                                            Returns:
                                            
-
                                            A list containing two parent individuals with predefined chromosomes and size.
                                            
-
                                            
-
                                            Return type:
                                            
-
                                            list
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-src.tests.test_unfair_average_crossover.setup_problem()[source]
                                            
-
                                            Fixture for creating a mock problem instance.
                                            
-
                                            
-
                                            Returns:
                                            
-
                                            An instance of the mock problem.
                                            
-
                                            
-
                                            Return type:
                                            
-
                                            MockProblem
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-src.tests.test_unfair_average_crossover.test_unfair_average_crossover(setup_parents, setup_problem)[source]
                                            
-
                                            Test the UnfairAvarageCrossover function implementation.
                                            
-
                                            This test checks the unfair average crossover on a pair of parent individuals by verifying the recombination
-operation and the integrity of the offspring chromosomes.
                                            
-
                                            
-
                                            Parameters:
                                            
-
                                              
-
                                            • setup_parents (fixture) – The fixture providing the parent individuals.
                                              • 
-
                                            • setup_problem (fixture) – The fixture providing the mock problem instance.
                                              • 
-
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_uniform_crossover module
                                            
-
                                            
-
                                            
-src.tests.test_uniform_crossover.test_uniform_crossover()[source]
                                            
-
                                            Test the UniformCrossover class implementation.
                                            
-
                                            This test verifies the functionality of the UniformCrossover class for binary chromosomes.
-It ensures that the crossover operation produces valid offspring with the expected chromosome size.
                                            
-
                                            The test performs the following checks:
-1. Both offspring have the same chromosome size as the parents.
-2. The chromosomes of the offspring contain only valid binary values (0 or 1).
                                            
-
                                            
-
                                            Raises:
                                            
-
                                            AssertionError – If any of the conditions for the crossover operation are not met.
                                            
-
                                            
-
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_zakharov_function module
                                            
-
                                            
-
                                            
-src.tests.test_zakharov_function.test_zakharov_function()[source]
                                            
-
                                            Test the Zakharov function implementation.
                                            
-
                                            This test checks the calculation of the Zakharov function value
-for given lists of float variables. It uses predefined inputs and compares
-the outputs to the expected values.
                                            
-
                                            
-
-
                                            
-
                                            
-
                                            src.tests.test_zettle_function module
                                            
-
                                            
-
                                            
-src.tests.test_zettle_function.setup_zettle()[source]
                                            
-
                                            Fixture to provide an instance of the Zettle problem.
                                            
-
                                            
-
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-src.tests.test_zettle_function.test_zettle_function(setup_zettle)[source]
                                            
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                                            Test the Zettle function implementation.
                                            
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                                            This test checks the calculation of the Zettle function value for given lists of float variables.
-It uses predefined inputs and compares the outputs to the expected values.
                                            
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                                            Parameters:
                                            
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                                            setup_zettle (fixture) – The fixture providing the Zettle problem instance.
                                            
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