From 3f5e3b789fff1f72f954544c90192441849be44d Mon Sep 17 00:00:00 2001 From: SevgiAkten Date: Tue, 10 Dec 2024 09:05:48 +0000 Subject: [PATCH] deploy: 12e3761401ffd18b63d5cb9d3ca9a0dc76513711 --- .../pycellga/example/example_alpha_cga.html | 17 +- _modules/pycellga/example/example_ccga.html | 8 +- _modules/pycellga/example/example_cga.html | 17 +- _modules/pycellga/example/example_mcccga.html | 8 +- .../pycellga/example/example_sync_cga.html | 17 +- _modules/pycellga/optimizer.html | 81 ------ _sources/usage_examples.rst.txt | 244 ++++++++++-------- searchindex.js | 2 +- usage_examples.html | 244 ++++++++++-------- 9 files changed, 328 insertions(+), 310 deletions(-) diff --git a/_modules/pycellga/example/example_alpha_cga.html b/_modules/pycellga/example/example_alpha_cga.html index a0e30bd..ee16717 100644 --- a/_modules/pycellga/example/example_alpha_cga.html +++ b/_modules/pycellga/example/example_alpha_cga.html @@ -336,10 +336,15 @@

Source code for pycellga.example.example_alpha_cga

import os sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) -import optimizer -from individual import GeneType from numpy import power as pw +from optimizer import alpha_cga +from individual import GeneType +from recombination import BlxalphaCrossover +from mutation import FloatUniformMutation +from selection import TournamentSelection + +
[docs] class ExampleProblem: @@ -398,7 +403,7 @@

Source code for pycellga.example.example_alpha_cga

problem_instance = ExampleProblem() # Run the optimizer and get the result - result = optimizer.alpha_cga( + result = alpha_cga( n_cols=5, n_rows=5, n_gen=100, @@ -407,9 +412,9 @@

Source code for pycellga.example.example_alpha_cga

p_crossover=0.9, p_mutation=0.2, problem=problem_instance, # Pass the ExampleProblem instance - selection=optimizer.TournamentSelection, - recombination=optimizer.BlxalphaCrossover, - mutation=optimizer.FloatUniformMutation, + selection=TournamentSelection, + recombination=BlxalphaCrossover, + mutation=FloatUniformMutation, mins=[-32.768] * 10, # Minimum values for each gene maxs=[32.768] * 10, # Maximum values for each gene seed_par=100 diff --git a/_modules/pycellga/example/example_ccga.html b/_modules/pycellga/example/example_ccga.html index 5547bf6..02efc06 100644 --- a/_modules/pycellga/example/example_ccga.html +++ b/_modules/pycellga/example/example_ccga.html @@ -336,7 +336,9 @@

Source code for pycellga.example.example_ccga

import os sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) -import optimizer +from optimizer import ccga +from individual import GeneType +from selection import TournamentSelection from individual import GeneType
@@ -396,14 +398,14 @@

Source code for pycellga.example.example_ccga

problem_instance = ExampleProblem() # Run the optimizer and get the result - result = optimizer.ccga( + result = ccga( n_cols=5, n_rows=5, n_gen=100, ch_size=10, gen_type=GeneType.BINARY, problem=problem_instance, # Pass the ExampleProblem instance - selection=optimizer.TournamentSelection, + selection=TournamentSelection, mins=[0] * 10, # Minimum values for each gene (binary) maxs=[1] * 10 # Maximum values for each gene (binary) ) diff --git a/_modules/pycellga/example/example_cga.html b/_modules/pycellga/example/example_cga.html index 6208d06..7cc47cb 100644 --- a/_modules/pycellga/example/example_cga.html +++ b/_modules/pycellga/example/example_cga.html @@ -335,10 +335,15 @@

Source code for pycellga.example.example_cga

import sys
 import os
 sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
-import optimizer
-from individual import GeneType
 from numpy import power as pw
 
+from optimizer import cga
+from individual import GeneType
+from recombination import ByteOnePointCrossover
+from mutation import ByteMutationRandom
+from selection import TournamentSelection
+
+
 

 [docs]
 class ExampleProblem:
@@ -396,7 +401,7 @@ 
Source code for pycellga.example.example_cga
# Create an instance of the problem
     problem_instance = ExampleProblem()
 
-    result = optimizer.cga(
+    result = cga(
         n_cols=5,
         n_rows=5,
         n_gen=100,
@@ -405,9 +410,9 @@ 
Source code for pycellga.example.example_cga
p_crossover=0.9,
         p_mutation=0.2,
         problem=problem_instance,  # Pass the ExampleProblem instance
-        selection=optimizer.TournamentSelection,
-        recombination=optimizer.ByteOnePointCrossover,
-        mutation=optimizer.ByteMutationRandom,
+        selection=TournamentSelection,
+        recombination=ByteOnePointCrossover,
+        mutation=ByteMutationRandom,
         mins=[-32.768] * 5,  # Minimum values for each gene
         maxs=[32.768] * 5,    # Maximum values for each gene
         seed_par=100
diff --git a/_modules/pycellga/example/example_mcccga.html b/_modules/pycellga/example/example_mcccga.html
index accee7a..832b4f9 100644
--- a/_modules/pycellga/example/example_mcccga.html
+++ b/_modules/pycellga/example/example_mcccga.html
@@ -336,9 +336,11 @@ 
Source code for pycellga.example.example_mcccga
<
 import os
 sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
 
-import optimizer
 import numpy as np
+
+from optimizer import mcccga
 from individual import GeneType
+from selection import TournamentSelection
 
 

 [docs]
@@ -398,14 +400,14 @@ 
Source code for pycellga.example.example_mcccga
<
     # Create an instance of the problem
     problem_instance = RealProblem()
 
-    result = optimizer.mcccga(
+    result = mcccga(
         n_cols=5,
         n_rows=5,
         n_gen=500,
         ch_size=5,
         gen_type=GeneType.REAL,
         problem=problem_instance,  # Pass the RealProblem instance
-        selection=optimizer.TournamentSelection,
+        selection=TournamentSelection,
         mins=[-3.768] * 5,
         maxs=[3.768] * 5
     )
diff --git a/_modules/pycellga/example/example_sync_cga.html b/_modules/pycellga/example/example_sync_cga.html
index 2483b02..1fd6ca9 100644
--- a/_modules/pycellga/example/example_sync_cga.html
+++ b/_modules/pycellga/example/example_sync_cga.html
@@ -336,10 +336,15 @@ 
Source code for pycellga.example.example_sync_cga
import os
 sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
 
-import optimizer
-from individual import GeneType
 from numpy import power as pw
 
+from optimizer import sync_cga
+from individual import GeneType
+from recombination import BlxalphaCrossover
+from mutation import FloatUniformMutation
+from selection import TournamentSelection
+
+
 

 [docs]
 class ExampleProblem:
@@ -397,7 +402,7 @@ 
Source code for pycellga.example.example_sync_cga
# Create an instance of the problem
     problem_instance = ExampleProblem()
 
-    result = optimizer.sync_cga(
+    result = sync_cga(
         n_cols=5,
         n_rows=5,
         n_gen=100,
@@ -406,9 +411,9 @@ 
Source code for pycellga.example.example_sync_cga
p_crossover=0.9,
         p_mutation=0.2,
         problem=problem_instance,  # Pass the ExampleProblem instance
-        selection=optimizer.TournamentSelection,
-        recombination=optimizer.BlxalphaCrossover,
-        mutation=optimizer.FloatUniformMutation,
+        selection=TournamentSelection,
+        recombination=BlxalphaCrossover,
+        mutation=FloatUniformMutation,
         mins=[-32.768] * 5,  # Minimum values for each gene
         maxs=[32.768] * 5,    # Maximum values for each gene
         seed_par=100
diff --git a/_modules/pycellga/optimizer.html b/_modules/pycellga/optimizer.html
index ea51527..33790e7 100644
--- a/_modules/pycellga/optimizer.html
+++ b/_modules/pycellga/optimizer.html
@@ -333,87 +333,6 @@
              
   
Source code for pycellga.optimizer
 
-# ------------------------------ selection --------------------------------- #
-from selection.roulette_wheel_selection import RouletteWheelSelection
-from selection.tournament_selection import TournamentSelection
-from selection.selection_operator import SelectionOperator
-# -------------------------------------------------------------------------- #
-
-# ------------------------------ recombination ----------------------------- #
-from recombination.one_point_crossover import OnePointCrossover
-from recombination.pmx_crossover import PMXCrossover
-from recombination.two_point_crossover import TwoPointCrossover
-from recombination.uniform_crossover import UniformCrossover
-from recombination.byte_uniform_crossover import ByteUniformCrossover
-from recombination.byte_one_point_crossover import ByteOnePointCrossover
-from recombination.flat_crossover import FlatCrossover
-from recombination.arithmetic_crossover import ArithmeticCrossover
-from recombination.blxalpha_crossover import BlxalphaCrossover
-from recombination.linear_crossover import LinearCrossover
-from recombination.unfair_avarage_crossover import UnfairAvarageCrossover
-from recombination.recombination_operator import RecombinationOperator
-# -------------------------------------------------------------------------- #
-
-# -------------------------------- mutation -------------------------------- #
-from mutation.bit_flip_mutation import BitFlipMutation
-from mutation.byte_mutation import ByteMutation
-from mutation.byte_mutation_random import ByteMutationRandom
-from mutation.insertion_mutation import InsertionMutation
-from mutation.shuffle_mutation import ShuffleMutation
-from mutation.swap_mutation import SwapMutation
-from mutation.two_opt_mutation import TwoOptMutation
-from mutation.float_uniform_mutation import FloatUniformMutation
-from mutation.mutation_operator import MutationOperator
-# -------------------------------------------------------------------------- #
-
-
-# ------------------- problems.single_objective.continuous -------------------------------- #
-from problems.single_objective.continuous.ackley import *
-from problems.single_objective.continuous.bentcigar import Bentcigar
-from problems.single_objective.continuous.bohachevsky import Bohachevsky
-from problems.single_objective.continuous.chichinadze import Chichinadze
-from problems.single_objective.continuous.dropwave import Dropwave
-from problems.single_objective.continuous.fms import Fms
-from problems.single_objective.continuous.griewank import Griewank
-from problems.single_objective.continuous.holzman import Holzman
-from problems.single_objective.continuous.levy import Levy
-from problems.single_objective.continuous.matyas import Matyas
-from problems.single_objective.continuous.pow import Pow
-from problems.single_objective.continuous.powell import Powell
-from problems.single_objective.continuous.rastrigin import Rastrigin
-from problems.single_objective.continuous.rosenbrock import Rosenbrock
-from problems.single_objective.continuous.rothellipsoid import Rothellipsoid
-from problems.single_objective.continuous.schaffer import Schaffer
-from problems.single_objective.continuous.schaffer2 import Schaffer2
-from problems.single_objective.continuous.schwefel import Schwefel
-from problems.single_objective.continuous.sphere import Sphere
-from problems.single_objective.continuous.styblinskitang import StyblinskiTang
-from problems.single_objective.continuous.sumofdifferentpowers import Sumofdifferentpowers
-from problems.single_objective.continuous.threehumps import Threehumps
-from problems.single_objective.continuous.zakharov import Zakharov
-from problems.single_objective.continuous.zettle import Zettle
-# ----------------------------------------------------------------------------------------------- #
-
-
-# ------------------- problems.single_objective.discrete.binary -------------------------------- #
-from problems.single_objective.discrete.binary.count_sat import CountSat
-from problems.single_objective.discrete.binary.ecc import Ecc
-from problems.single_objective.discrete.binary.maxcut20_01 import Maxcut20_01
-from problems.single_objective.discrete.binary.maxcut20_09 import Maxcut20_09
-from problems.single_objective.discrete.binary.maxcut100 import Maxcut100
-from problems.single_objective.discrete.binary.mmdp import Mmdp
-from problems.single_objective.discrete.binary.one_max import OneMax
-from problems.single_objective.discrete.binary.peak import Peak
-# ----------------------------------------------------------------------------------------------- #
-
-
-# ------------------- problems.single_objective.discrete.permutation.tsp ------------------------ #
-from problems.single_objective.discrete.permutation.tsp import Tsp
-# ----------------------------------------------------------------------------------------------- #
-
-
-
-
 import random
 import numpy as np
 import byte_operators
diff --git a/_sources/usage_examples.rst.txt b/_sources/usage_examples.rst.txt
index 6149d26..0ee3348 100644
--- a/_sources/usage_examples.rst.txt
+++ b/_sources/usage_examples.rst.txt
@@ -17,7 +17,12 @@ Here’s how we can define this problem in Python using the `ExampleProblem` cla
 .. code-block:: python
 
     from mpmath import power as pw
-    import pycellga 
+
+    from pycellga.optimizer import cga
+    from pycellga.individual import GeneType
+    from pycellga.selection import TournamentSelection
+    from pycellga.recombination import ByteOnePointCrossover
+    from pycellga.mutation import ByteMutationRandom
 
     class ExampleProblem:
         
@@ -31,18 +36,18 @@ Here’s how we can define this problem in Python using the `ExampleProblem` cla
 
 .. code-block:: python
 
-    result = pycellga.optimizer.cga(
+    result = cga(
         n_cols = 5,
         n_rows = 5,
         n_gen = 100,
         ch_size = 5,
-        gen_type = pycellga.optimizer.GeneType.REAL,
+        gen_type = GeneType.REAL,
         p_crossover = 0.9,
         p_mutation = 0.2,
         problem = ExampleProblem(),  # Replace with a real problem instance as needed
-        selection = pycellga.optimizer.TournamentSelection,
-        recombination = pycellga.optimizer.ByteOnePointCrossover,
-        mutation = pycellga.optimizer.ByteMutationRandom,
+        selection = TournamentSelection,
+        recombination = ByteOnePointCrossover,
+        mutation = ByteMutationRandom,
         mins = [-32.768] * 5,  # Minimum values for each gene
         maxs = [32.768] * 5,    # Maximum values for each gene
         seed_par = 100 # Ensures the random number generation is repeatable
@@ -67,7 +72,12 @@ The Alpha Cellular Genetic Algorithm optimizes a real-valued problem using Blxal
 .. code-block:: python
 
     from mpmath import power as pw
-    import pycellga 
+
+    from pycellga.optimizer import alpha_cga
+    from pycellga.individual import GeneType
+    from pycellga.selection import TournamentSelection
+    from pycellga.recombination import BlxalphaCrossover
+    from pycellga.mutation import FloatUniformMutation
 
     class ExampleProblem:
         
@@ -78,18 +88,18 @@ The Alpha Cellular Genetic Algorithm optimizes a real-valued problem using Blxal
             return sum(pw(xi, 2) for xi in x)
 
 
-    result = pycellga.optimizer.alpha_cga(
+    result = alpha_cga(
         n_cols=5,
         n_rows=5,
         n_gen=100,
         ch_size=10,
-        gen_type=pycellga.optimizer.GeneType.REAL,
+        gen_type=GeneType.REAL,
         p_crossover=0.9,
         p_mutation=0.2,
         problem=ExampleProblem(),
-        selection=pycellga.optimizer.TournamentSelection,
-        recombination=pycellga.optimizer.BlxalphaCrossover,
-        mutation=pycellga.optimizer.FloatUniformMutation,
+        selection=TournamentSelection,
+        recombination=BlxalphaCrossover,
+        mutation=FloatUniformMutation,
         mins=[-15] * 10,
         maxs=[15] * 10,
         seed_par=100
@@ -105,7 +115,9 @@ The Compact Cellular Genetic Algorithm optimizes a binary problem where the goal
 
 .. code-block:: python
 
-    import pycellga 
+    from pycellga.optimizer import ccga
+    from pycellga.individual import GeneType
+    from pycellga.selection import TournamentSelection
 
     class ExampleProblem:
         def __init__(self):
@@ -114,14 +126,14 @@ The Compact Cellular Genetic Algorithm optimizes a binary problem where the goal
         def f(self, x):
             return sum(x)
 
-    result = pycellga.optimizer.ccga(
+    result = ccga(
         n_cols=5,
         n_rows=5,
         n_gen=100,
         ch_size=10,
-        gen_type=pycellga.optimizer.GeneType.BINARY,
+        gen_type=GeneType.BINARY,
         problem=ExampleProblem(),
-        selection=pycellga.optimizer.TournamentSelection,
+        selection=TournamentSelection,
         mins=[0] * 10,
         maxs=[1] * 10
     )
@@ -135,7 +147,10 @@ The Machine-Coded Compact Cellular Genetic Algorithm solves real-valued optimiza
 
 .. code-block:: python
 
-    import pycellga
+    from pycellga.optimizer import mcccga
+    from pycellga.individual import GeneType
+    from pycellga.selection import TournamentSelection
+    
 
     class RealProblem:
         def __init__(self):
@@ -144,14 +159,14 @@ The Machine-Coded Compact Cellular Genetic Algorithm solves real-valued optimiza
         def f(self, x):
             return sum(np.power(xi, 2) for xi in x)
 
-    result = pycellga.optimizer.mcccga(
+    result = mcccga(
         n_cols=5,
         n_rows=5,
         n_gen=500,
         ch_size=5,
-        gen_type=pycellga.optimizer.GeneType.REAL,
+        gen_type=GeneType.REAL,
         problem=RealProblem(),
-        selection=pycellga.optimizer.TournamentSelection,
+        selection=TournamentSelection,
         mins=[-3.55] * 5,
         maxs=[3.55] * 5
     )
@@ -165,7 +180,11 @@ The Synchronous Cellular Genetic Algorithm optimizes a real-valued problem in a
 
 .. code-block:: python
 
-    import pycellga
+    from pycellga.optimizer import sync_cga
+    from pycellga.individual import GeneType
+    from pycellga.selection import TournamentSelection
+    from pycellga.recombination import BlxalphaCrossover
+    from pycellga.mutation import FloatUniformMutation
 
     class ExampleProblem:
         def __init__(self):
@@ -174,18 +193,18 @@ The Synchronous Cellular Genetic Algorithm optimizes a real-valued problem in a
         def f(self, x):
             return sum(pw(xi, 2) for xi in x)
 
-    result = pycellga.optimizer.sync_cga(
+    result = sync_cga(
         n_cols=5,
         n_rows=5,
         n_gen=100,
         ch_size=5,
-        gen_type=pycellga.optimizer.GeneType.REAL,
+        gen_type=GeneType.REAL,
         p_crossover=0.9,
         p_mutation=0.2,
         problem=ExampleProblem(),
-        selection=pycellga.optimizer.TournamentSelection,
-        recombination=pycellga.optimizer.BlxalphaCrossover,
-        mutation=pycellga.optimizer.FloatUniformMutation,
+        selection=TournamentSelection,
+        recombination=BlxalphaCrossover,
+        mutation=FloatUniformMutation,
         mins=[-32.768] * 5,
         maxs=[32.768] * 5,
         seed_par=100
@@ -203,9 +222,9 @@ Customization Scenarios
 
 You can change the `gen_type` parameter based on the type of problem:
 
-- `pycellga.optimizer.GeneType.REAL` for real-valued problems.
-- `pycellga.optimizer.GeneType.BINARY` for binary problems.
-- `pycellga.optimizer.GeneType.PERMUTATION` for permutation-based problems.
+- `pycellga.individual.GeneType.REAL` for real-valued problems.
+- `pycellga.individual.GeneType.BINARY` for binary problems.
+- `pycellga.individual.GeneType.PERMUTATION` for permutation-based problems.
 
 
 **Problem Selection**
@@ -216,49 +235,49 @@ You can change the `gen_type` parameter based on the type of problem:
 
 For continuous optimization problems, the following functions are available:
 
-- `pycellga.optimizer.Ackley()`
-- `pycellga.optimizer.Bentcigar()`
-- `pycellga.optimizer.Bohachevsky()`
-- `pycellga.optimizer.Chichinadze()`
-- `pycellga.optimizer.Dropwave()`
-- `pycellga.optimizer.Fms()`
-- `pycellga.optimizer.Griewank()`
-- `pycellga.optimizer.Holzman()`
-- `pycellga.optimizer.Levy()`
-- `pycellga.optimizer.Matyas()`
-- `pycellga.optimizer.Pow()`
-- `pycellga.optimizer.Powell()`
-- `pycellga.optimizer.Rastrigin()`
-- `pycellga.optimizer.Rosenbrock()`
-- `pycellga.optimizer.Rothellipsoid()`
-- `pycellga.optimizer.Schaffer()`
-- `pycellga.optimizer.Schaffer2()`
-- `pycellga.optimizer.Schwefel()`
-- `pycellga.optimizer.Sphere()`
-- `pycellga.optimizer.StyblinskiTang()`
-- `pycellga.optimizer.Sumofdifferentpowers()`
-- `pycellga.optimizer.Threehumps()`
-- `pycellga.optimizer.Zakharov()`
-- `pycellga.optimizer.Zettle()`
+- `pycellga.problems.Ackley`
+- `pycellga.problems.Bentcigar`
+- `pycellga.problems.Bohachevsky`
+- `pycellga.problems.Chichinadze`
+- `pycellga.problems.Dropwave`
+- `pycellga.problems.Fms`
+- `pycellga.problems.Griewank`
+- `pycellga.problems.Holzman`
+- `pycellga.problems.Levy`
+- `pycellga.problems.Matyas`
+- `pycellga.problems.Pow`
+- `pycellga.problems.Powell`
+- `pycellga.problems.Rastrigin`
+- `pycellga.problems.Rosenbrock`
+- `pycellga.problems.Rothellipsoid`
+- `pycellga.problems.Schaffer`
+- `pycellga.problems.Schaffer2`
+- `pycellga.problems.Schwefel`
+- `pycellga.problems.Sphere`
+- `pycellga.problems.StyblinskiTang`
+- `pycellga.problems.Sumofdifferentpowers`
+- `pycellga.problems.Threehumps`
+- `pycellga.problems.Zakharov`
+- `pycellga.problems.Zettle`
 
 **Binary Problems**
 
 For binary optimization problems, use the following built-in options:
 
-- `pycellga.optimizer.CountSat()`
-- `pycellga.optimizer.Ecc()`
-- `pycellga.optimizer.Maxcut20_01()`
-- `pycellga.optimizer.Maxcut20_09()`
-- `pycellga.optimizer.Maxcut100()`
-- `pycellga.optimizer.Mmdp()`
-- `pycellga.optimizer.OneMax()`
-- `pycellga.optimizer.Peak()`
+- `pycellga.problems.CountSat`
+- `pycellga.problems.Ecc`
+- `pycellga.problems.Maxcut20_01`
+- `pycellga.problems.Maxcut20_09`
+- `pycellga.problems.Maxcut100`
+- `pycellga.problems.Mmdp`
+- `pycellga.problems.OneMax`
+- `pycellga.problems.Peak`
 
 **Permutation Problems**
 
 For permutation-based optimization problems, the following option is available:
 
-- `pycellga.optimizer.Tsp()`
+- `pycellga.problems.Tsp`
 
 These built-in problems provide a diverse set of test cases, allowing users to explore `pycellga`'s capabilities across a wide range of optimization challenges. Users can also define custom problems to suit their specific needs.
 
@@ -267,38 +286,38 @@ These built-in problems provide a diverse set of test cases, allowing users to e
 
 Choose from a variety of selection methods:
 
-- `pycellga.optimizer.TournamentSelection`
-- `pycellga.optimizer.RouletteWheelSelection`
+- `pycellga.selection.TournamentSelection`
+- `pycellga.selection.RouletteWheelSelection`
 
 **Recombination Operators**
 
 The package provides multiple crossover operators:
 
-- `pycellga.optimizer.OnePointCrossover`
-- `pycellga.optimizer.PMXCrossover`
-- `pycellga.optimizer.TwoPointCrossover`
-- `pycellga.optimizer.UniformCrossover`
-- `pycellga.optimizer.ByteUniformCrossover`
-- `pycellga.optimizer.ByteOnePointCrossover`
-- `pycellga.optimizer.FlatCrossover`
-- `pycellga.optimizer.ArithmeticCrossover`
-- `pycellga.optimizer.BlxalphaCrossover`
-- `pycellga.optimizer.LinearCrossover`
-- `pycellga.optimizer.UnfairAvarageCrossover`
+- `pycellga.recombination.OnePointCrossover`
+- `pycellga.recombination.PMXCrossover`
+- `pycellga.recombination.TwoPointCrossover`
+- `pycellga.recombination.UniformCrossover`
+- `pycellga.recombination.ByteUniformCrossover`
+- `pycellga.recombination.ByteOnePointCrossover`
+- `pycellga.recombination.FlatCrossover`
+- `pycellga.recombination.ArithmeticCrossover`
+- `pycellga.recombination.BlxalphaCrossover`
+- `pycellga.recombination.LinearCrossover`
+- `pycellga.recombination.UnfairAvarageCrossover`
 
 **Mutation Operators**
 
 You can customize mutation with these options:
 
-- `pycellga.optimizer.BitFlipMutation`
-- `pycellga.optimizer.ByteMutation`
-- `pycellga.optimizer.ByteMutationRandom`
-- `pycellga.optimizer.InsertionMutation`
-- `pycellga.optimizer.ShuffleMutation`
-- `pycellga.optimizer.SwapMutation`
-- `pycellga.optimizer.TwoOptMutation`
-- `pycellga.optimizer.FloatUniformMutation`
-- `pycellga.optimizer.MutationOperator`
+- `pycellga.mutation.BitFlipMutation`
+- `pycellga.mutation.ByteMutation`
+- `pycellga.mutation.ByteMutationRandom`
+- `pycellga.mutation.InsertionMutation`
+- `pycellga.mutation.ShuffleMutation`
+- `pycellga.mutation.SwapMutation`
+- `pycellga.mutation.TwoOptMutation`
+- `pycellga.mutation.FloatUniformMutation`
+- `pycellga.mutation.MutationOperator`
 
 
 **Example Scenarios**
@@ -309,18 +328,25 @@ Optimize a binary problem using tournament selection, one-point crossover, and b
 
 .. code-block:: python
 
-    result = pycellga.optimizer.cga(
+    from pycellga.optimizer import cga
+    from pycellga.individual import GeneType
+    from pycellga.problems import OneMax
+    from pycellga.selection import TournamentSelection
+    from pycellga.recombination import OnePointCrossover
+    from pycellga.mutation import BitFlipMutation
+
+    result = cga(
         n_cols=5,
         n_rows=5,
         n_gen=100,
         ch_size=10,
-        gen_type=pycellga.optimizer.GeneType.BINARY,
+        gen_type=GeneType.BINARY,
         p_crossover=0.8,
         p_mutation=0.1,
-        problem=pycellga.optimizer.OneMax(),  # Built-in binary optimization problem
-        selection=pycellga.optimizer.TournamentSelection,
-        recombination=pycellga.optimizer.OnePointCrossover,
-        mutation=pycellga.optimizer.BitFlipMutation,
+        problem=OneMax(),  # Built-in binary optimization problem
+        selection=TournamentSelection,
+        recombination=OnePointCrossover,
+        mutation=BitFlipMutation,
         mins=[0] * 10,
         maxs=[1] * 10,
         seed_par=100
@@ -335,18 +361,25 @@ Solve a real-valued optimization problem using Byte One Point Crossover and Byte
 
 .. code-block:: python
 
-    result = pycellga.optimizer.cga(
+    from pycellga.optimizer import cga
+    from pycellga.individual import GeneType
+    from pycellga.problems import Ackley
+    from pycellga.selection import RouletteWheelSelection
+    from pycellga.recombination import ByteOnePointCrossover
+    from pycellga.mutation import ByteMutation
+
+    result = cga(
         n_cols=5,
         n_rows=5,
         n_gen=100,
         ch_size=10,
-        gen_type=pycellga.optimizer.GeneType.REAL,
+        gen_type=GeneType.REAL,
         p_crossover=0.9,
         p_mutation=0.2,
-        problem=pycellga.optimizer.Ackley(10),  # Built-in real-valued optimization problem
-        selection=pycellga.optimizer.RouletteWheelSelection,
-        recombination=pycellga.optimizer.ByteOnePointCrossover,
-        mutation=pycellga.optimizer.ByteMutation,
+        problem=Ackley(10),  # Built-in real-valued optimization problem
+        selection=RouletteWheelSelection,
+        recombination=ByteOnePointCrossover,
+        mutation=ByteMutation,
         mins=[-32.768] * 10,
         maxs=[32.768] * 10,
         seed_par=100
@@ -360,18 +393,25 @@ Optimize a TSP using permutation encoding, PMX crossover, and swap mutation.
 
 .. code-block:: python
 
-    result = pycellga.optimizer.cga(
+    from pycellga.optimizer import cga
+    from pycellga.individual import GeneType
+    from pycellga.problems import Tsp
+    from pycellga.selection import TournamentSelection
+    from pycellga.recombination import PMXCrossover
+    from pycellga.mutation import SwapMutation
+
+    result = cga(
         n_cols=5,
         n_rows=5,
         n_gen=300,
         ch_size=14,  # Number of cities
-        gen_type=pycellga.optimizer.GeneType.PERMUTATION,
+        gen_type=GeneType.PERMUTATION,
         p_crossover=0.85,
         p_mutation=0.15,
-        problem=pycellga.optimizer.Tsp(),  # Built-in TSP optimization problem
-        selection=pycellga.optimizer.TournamentSelection,
-        recombination=pycellga.optimizer.PMXCrossover,
-        mutation=pycellga.optimizer.SwapMutation,
+        problem=Tsp(),  # Built-in TSP optimization problem
+        selection=TournamentSelection,
+        recombination=PMXCrossover,
+        mutation=SwapMutation,
         mins=[1] * 14,
         maxs=[14] * 14,
         seed_par=100
diff --git a/searchindex.js b/searchindex.js
index f3a6de4..995c4fe 100644
--- a/searchindex.js
+++ b/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"alltitles": {"Abstract Problem Base": [[8, "abstract-problem-base"]], "Ackley Function": [[10, "ackley-function"]], "Advanced Usage Examples": [[17, "advanced-usage-examples"]], "Arithmetic Crossover": [[14, "arithmetic-crossover"]], "Available Example Modules": [[5, "available-example-modules"]], "BLX-Alpha Crossover": [[14, "blx-alpha-crossover"]], "Bent Cigar Function": [[10, "bent-cigar-function"]], "Binary Optimization Problems": [[12, null]], "Binary-Based Problems": [[11, "binary-based-problems"]], "Bit Flip Mutation": [[6, "bit-flip-mutation"]], "Bohachevsky Function": [[10, "bohachevsky-function"]], "Byte One-Point Crossover": [[14, "byte-one-point-crossover"]], "Byte Operators": [[4, "byte-operators"]], "Byte Uniform Crossover": [[14, "byte-uniform-crossover"]], "Byte-Level Mutation": [[6, "byte-level-mutation"]], "Chichinadze Function": [[10, "chichinadze-function"]], "Citing": [[0, null]], "Coding Standards": [[1, "coding-standards"]], "Compact 13": [[7, "compact-13"]], "Compact 21": [[7, "compact-21"]], "Compact 25": [[7, "compact-25"]], "Compact 9": [[7, "compact-9"]], "Continuous Optimization Problems": [[9, "continuous-optimization-problems"], [10, null]], "Contributing": [[1, null]], "Core Modules": [[4, "core-modules"]], "Count SAT": [[12, "count-sat"]], "Customization Scenarios": [[17, "customization-scenarios"]], "Development Setup": [[1, "development-setup"]], "Discrete Optimization Problems": [[9, "discrete-optimization-problems"], [11, null]], "Drop Wave Function": [[10, "drop-wave-function"]], "ECC Problem": [[12, "ecc-problem"]], "Example Implementations in pycellga": [[5, null]], "Flat Crossover": [[14, "flat-crossover"]], "Fletcher-Powell (FMS) Binary Problem": [[12, "fletcher-powell-fms-binary-problem"]], "Frequency Modulation Sound Function (FMS)": [[10, "frequency-modulation-sound-function-fms"]], "Grid Structure": [[4, "grid-structure"]], "Griewank Function": [[10, "griewank-function"]], "Holzman Function": [[10, "holzman-function"]], "Individual Representation": [[4, "individual-representation"]], "Insertion-Based Mutation": [[6, "insertion-based-mutation"]], "Installation": [[3, null]], "Installing from PyPI": [[3, "installing-from-pypi"]], "Installing from Source": [[3, "installing-from-source"]], "Levy Function": [[10, "levy-function"]], "Linear 5": [[7, "linear-5"]], "Linear 9": [[7, "linear-9"]], "Linear Crossover": [[14, "linear-crossover"]], "Matyas Function": [[10, "matyas-function"]], "Max-Cut (100 nodes)": [[12, "max-cut-100-nodes"]], "Max-Cut (20 nodes, Density 0.1)": [[12, "max-cut-20-nodes-density-0-1"]], "Max-Cut (20 nodes, Density 0.9)": [[12, "max-cut-20-nodes-density-0-9"]], "Multi-modal Deceptive Problem (MMDP)": [[12, "multi-modal-deceptive-problem-mmdp"]], "Mutation Operators": [[6, null]], "Neighborhood Operators": [[7, null]], "One-Max Problem": [[12, "one-max-problem"]], "One-Point Crossover": [[14, "one-point-crossover"]], "Optimizer": [[4, "optimizer"]], "Optional Dependencies": [[3, "optional-dependencies"]], "PYCELLGA Documentation": [[2, null]], "Partially Matched Crossover (PMX)": [[14, "partially-matched-crossover-pmx"]], "Peak Problem": [[12, "peak-problem"]], "Permutation-Based Optimization Problems": [[13, null]], "Permutation-Based Problems": [[11, "permutation-based-problems"]], "Population Management": [[4, "population-management"]], "Pow Function": [[10, "pow-function"]], "Powell Function": [[10, "powell-function"]], "Problem Definitions": [[8, null]], "Pull Request Guidelines": [[1, "pull-request-guidelines"]], "Randomized Byte Mutation": [[6, "randomized-byte-mutation"]], "Rastrigin Function": [[10, "rastrigin-function"]], "Recombination Operators": [[14, null]], "Requirements": [[3, "requirements"]], "Rosenbrock Function": [[10, "rosenbrock-function"]], "Rothellipsoid Function": [[10, "rothellipsoid-function"]], "Roulette Wheel Selection": [[15, "roulette-wheel-selection"]], "Running Tests": [[1, "running-tests"]], "Schaffer Function": [[10, "schaffer-function"]], "Schaffer2 Function": [[10, "schaffer2-function"]], "Schwefel Function": [[10, "schwefel-function"]], "Selection Operators": [[15, null]], "Shuffle Mutation": [[6, "shuffle-mutation"]], "Single-Objective Optimization Problems": [[9, null]], "Single-Objective Problems": [[8, "single-objective-problems"]], "Sphere Function": [[10, "sphere-function"]], "Styblinskitang Function": [[10, "styblinskitang-function"]], "Sumofdifferentpowers Function": [[10, "sumofdifferentpowers-function"]], "Swap Mutation": [[6, "swap-mutation"]], "Table of Contents :": [[2, null]], "Testing Guidelines": [[1, "testing-guidelines"]], "Threehumps Function": [[10, "threehumps-function"]], "Tournament Selection": [[15, "tournament-selection"]], "Traveling Salesman Problem (TSP)": [[13, "traveling-salesman-problem-tsp"]], "Troubleshooting": [[3, "troubleshooting"]], "Two-Opt Mutation": [[6, "two-opt-mutation"]], "Two-Point Crossover": [[14, "two-point-crossover"]], "Unfair Average Crossover": [[14, "unfair-average-crossover"]], "Uniform Crossover": [[14, "uniform-crossover"]], "Uniform Float Mutation": [[6, "uniform-float-mutation"]], "Uninstallation": [[3, "uninstallation"]], "Usage Examples": [[17, null]], "Verifying the Installation": [[3, "verifying-the-installation"]], "Ways to Contribute": [[1, "ways-to-contribute"]], "Zakharov Function": [[10, "zakharov-function"]], "Zettle Function": [[10, "zettle-function"]], "pycellga: API Reference": [[4, null]], "setup module": [[16, null]]}, "docnames": ["citing", "contributing", "index", "installation", "pycellga", "pycellga.example", "pycellga.mutation", "pycellga.neighborhoods", "pycellga.problems", "pycellga.problems.single_objective", "pycellga.problems.single_objective.continuous", "pycellga.problems.single_objective.discrete", "pycellga.problems.single_objective.discrete.binary", "pycellga.problems.single_objective.discrete.permutation", "pycellga.recombination", "pycellga.selection", "setup", 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"pycellga.problems.abstract_problem.AbstractProblem.__init__", false]], "__init__() (ackley method)": [[10, "pycellga.problems.single_objective.continuous.ackley.Ackley.__init__", false]], "__init__() (arithmeticcrossover method)": [[14, "pycellga.recombination.arithmetic_crossover.ArithmeticCrossover.__init__", false]], "__init__() (bentcigar method)": [[10, "pycellga.problems.single_objective.continuous.bentcigar.Bentcigar.__init__", false]], "__init__() (bitflipmutation method)": [[6, "pycellga.mutation.bit_flip_mutation.BitFlipMutation.__init__", false]], "__init__() (blxalphacrossover method)": [[14, "pycellga.recombination.blxalpha_crossover.BlxalphaCrossover.__init__", false]], "__init__() (bohachevsky method)": [[10, "pycellga.problems.single_objective.continuous.bohachevsky.Bohachevsky.__init__", false]], "__init__() (bytemutation method)": [[6, "pycellga.mutation.byte_mutation.ByteMutation.__init__", false]], "__init__() (bytemutationrandom method)": [[6, 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(sumofdifferentpowers method)": [[10, "pycellga.problems.single_objective.continuous.sumofdifferentpowers.Sumofdifferentpowers.__init__", false]], "__init__() (swapmutation method)": [[6, "pycellga.mutation.swap_mutation.SwapMutation.__init__", false]], "__init__() (threehumps method)": [[10, "pycellga.problems.single_objective.continuous.threehumps.Threehumps.__init__", false]], "__init__() (tournamentselection method)": [[15, "pycellga.selection.tournament_selection.TournamentSelection.__init__", false]], "__init__() (tsp method)": [[13, "pycellga.problems.single_objective.discrete.permutation.tsp.Tsp.__init__", false]], "__init__() (twooptmutation method)": [[6, "pycellga.mutation.two_opt_mutation.TwoOptMutation.__init__", false]], "__init__() (twopointcrossover method)": [[14, "pycellga.recombination.two_point_crossover.TwoPointCrossover.__init__", false]], "__init__() (unfairavaragecrossover method)": [[14, 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pycellga.recombination.arithmetic_crossover)": [[14, "pycellga.recombination.arithmetic_crossover.ArithmeticCrossover", false]], "bentcigar (class in pycellga.problems.single_objective.continuous.bentcigar)": [[10, "pycellga.problems.single_objective.continuous.bentcigar.Bentcigar", false]], "binary (genetype attribute)": [[4, "pycellga.individual.GeneType.BINARY", false]], "bitflipmutation (class in pycellga.mutation.bit_flip_mutation)": [[6, "pycellga.mutation.bit_flip_mutation.BitFlipMutation", false]], "bits_to_float() (in module pycellga.byte_operators)": [[4, "pycellga.byte_operators.bits_to_float", false]], "bits_to_floats() (in module pycellga.byte_operators)": [[4, "pycellga.byte_operators.bits_to_floats", false]], "blxalphacrossover (class in pycellga.recombination.blxalpha_crossover)": [[14, "pycellga.recombination.blxalpha_crossover.BlxalphaCrossover", false]], "bohachevsky (class in pycellga.problems.single_objective.continuous.bohachevsky)": [[10, 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[0, 1, 2, 4, 5, 6, 8, 9, 10, 12, 14, 17], "variabl": [8, 10, 12, 13], "variant": [4, 5], "variat": 6, "varieti": 17, "variou": [0, 5, 7, 8, 10, 14, 15], "vector": [4, 17], "verifi": 1, "version": [3, 5, 12], "via": 3, "visibl": 0, "visit": [1, 13], "visual": 3, "volum": 0, "wa": 5, "wai": 3, "want": 17, "wave": 9, "we": [1, 17], "weight": 12, "went": 0, "what": 1, "when": [1, 5, 6, 17], "where": [4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17], "whether": 1, "which": [4, 5, 8, 10, 12, 14], "while": [6, 7], "whose": 7, "why": 0, "wide": [10, 11, 13, 14, 15, 17], "win": 4, "winner": 4, "wise": 6, "within": [4, 6, 7, 10, 12, 14], "without": 6, "work": [0, 11], "world": 17, "wrap": [6, 7], "wrapper": 10, "write": 1, "x": [4, 5, 7, 8, 10, 12, 13, 17], "x1": 10, "x2": 10, "x_i": 10, "xi": [10, 17], "y": [4, 7, 10], "year": 0, "yet": 6, "you": [0, 1, 3, 17], "your": [0, 1, 17], "yourself": 1, "zakharov": [9, 17], "zettl": [9, 17]}, "titles": ["Citing", "Contributing", "PYCELLGA Documentation", "Installation", "pycellga: API Reference", "Example Implementations in pycellga", "Mutation Operators", "Neighborhood Operators", "Problem Definitions", "Single-Objective Optimization Problems", "Continuous Optimization Problems", "Discrete Optimization Problems", "Binary Optimization Problems", "Permutation-Based Optimization Problems", "Recombination Operators", "Selection Operators", "setup module", "Usage Examples"], "titleterms": {"0": 12, "1": 12, "100": 12, "13": 7, "20": 12, "21": 7, "25": 7, "5": 7, "9": [7, 12], "One": [12, 14], "abstract": 8, "acklei": 10, "advanc": 17, "alpha": 14, "api": 4, "arithmet": 14, "avail": 5, "averag": 14, "base": [6, 8, 11, 13], "bent": 10, "binari": [11, 12], "bit": 6, "blx": 14, "bohachevski": 10, "byte": [4, 6, 14], "chichinadz": 10, "cigar": 10, "cite": 0, "code": 1, "compact": 7, "content": 2, "continu": [9, 10], "contribut": 1, "core": 4, "count": 12, "crossov": 14, "custom": 17, "cut": 12, "decept": 12, "definit": 8, "densiti": 12, "depend": 3, "develop": 1, "discret": [9, 11], "document": 2, "drop": 10, "ecc": 12, "exampl": [5, 17], "flat": 14, "fletcher": 12, "flip": 6, "float": 6, "fm": [10, 12], "frequenc": 10, "from": 3, "function": 10, "grid": 4, "griewank": 10, "guidelin": 1, "holzman": 10, "implement": 5, "individu": 4, "insert": 6, "instal": 3, "level": 6, "levi": 10, "linear": [7, 14], "manag": 4, "match": 14, "matya": 10, "max": 12, "mmdp": 12, "modal": 12, "modul": [4, 5, 10, 16], "multi": 12, "mutat": 6, "neighborhood": 7, "node": 12, "object": [8, 9], "oper": [4, 6, 7, 14, 15], "opt": 6, "optim": [4, 9, 10, 11, 12, 13], "option": 3, "partial": 14, "peak": 12, "permut": [11, 13], "pmx": 14, "point": 14, "popul": 4, "pow": 10, "powel": [10, 12], "problem": [8, 9, 10, 11, 12, 13], "pull": 1, "pycellga": [2, 4, 5], "pypi": 3, "random": 6, "rastrigin": 10, "recombin": 14, "refer": 4, "represent": 4, "request": 1, "requir": 3, "rosenbrock": 10, "rothellipsoid": 10, "roulett": 15, "run": 1, "salesman": 13, "sat": 12, "scenario": 17, "schaffer": 10, "schaffer2": 10, "schwefel": 10, "select": 15, "setup": [1, 16], "shuffl": 6, "singl": [8, 9], "sound": 10, "sourc": 3, "sphere": 10, "standard": 1, "structur": 4, "styblinskitang": 10, "sumofdifferentpow": 10, "swap": 6, "tabl": 2, "test": 1, "threehump": 10, "tournament": 15, "travel": 13, "troubleshoot": 3, "tsp": 13, "two": [6, 14], "unfair": 14, "uniform": [6, 14], "uninstal": 3, "usag": 17, "verifi": 3, "wai": 1, "wave": 10, "wheel": 15, "zakharov": 10, "zettl": 10}})
\ No newline at end of file
diff --git a/usage_examples.html b/usage_examples.html
index 0abc8a7..2682251 100644
--- a/usage_examples.html
+++ b/usage_examples.html
@@ -343,7 +343,12 @@ 
Usage Examples
from mpmath import power as pw
-import pycellga
+
+from pycellga.optimizer import cga
+from pycellga.individual import GeneType
+from pycellga.selection import TournamentSelection
+from pycellga.recombination import ByteOnePointCrossover
+from pycellga.mutation import ByteMutationRandom
 
 class ExampleProblem:
 
@@ -355,18 +360,18 @@ 
Usage Examples
result = pycellga.optimizer.cga(
+
result = cga(
     n_cols = 5,
     n_rows = 5,
     n_gen = 100,
     ch_size = 5,
-    gen_type = pycellga.optimizer.GeneType.REAL,
+    gen_type = GeneType.REAL,
     p_crossover = 0.9,
     p_mutation = 0.2,
     problem = ExampleProblem(),  # Replace with a real problem instance as needed
-    selection = pycellga.optimizer.TournamentSelection,
-    recombination = pycellga.optimizer.ByteOnePointCrossover,
-    mutation = pycellga.optimizer.ByteMutationRandom,
+    selection = TournamentSelection,
+    recombination = ByteOnePointCrossover,
+    mutation = ByteMutationRandom,
     mins = [-32.768] * 5,  # Minimum values for each gene
     maxs = [32.768] * 5,    # Maximum values for each gene
     seed_par = 100 # Ensures the random number generation is repeatable
@@ -385,7 +390,12 @@ 
Advanced Usage ExamplesExample 1: Alpha Cellular Genetic Algorithm (alpha_cga)

 
The Alpha Cellular Genetic Algorithm optimizes a real-valued problem using Blxalpha Crossover  and Float Uniform Mutation operators.

 
from mpmath import power as pw
-import pycellga
+
+from pycellga.optimizer import alpha_cga
+from pycellga.individual import GeneType
+from pycellga.selection import TournamentSelection
+from pycellga.recombination import BlxalphaCrossover
+from pycellga.mutation import FloatUniformMutation
 
 class ExampleProblem:
 
@@ -396,18 +406,18 @@ 
Advanced Usage Examplesreturn sum(pw(xi, 2) for xi in x)
 
 
-result = pycellga.optimizer.alpha_cga(
+result = alpha_cga(
     n_cols=5,
     n_rows=5,
     n_gen=100,
     ch_size=10,
-    gen_type=pycellga.optimizer.GeneType.REAL,
+    gen_type=GeneType.REAL,
     p_crossover=0.9,
     p_mutation=0.2,
     problem=ExampleProblem(),
-    selection=pycellga.optimizer.TournamentSelection,
-    recombination=pycellga.optimizer.BlxalphaCrossover,
-    mutation=pycellga.optimizer.FloatUniformMutation,
+    selection=TournamentSelection,
+    recombination=BlxalphaCrossover,
+    mutation=FloatUniformMutation,
     mins=[-15] * 10,
     maxs=[15] * 10,
     seed_par=100
@@ -419,7 +429,9 @@ 
Advanced Usage Examples—

 
Example 2: Compact Cellular Genetic Algorithm (ccga)

 
The Compact Cellular Genetic Algorithm optimizes a binary problem where the goal is to maximize the number of `1`s.

-
import pycellga
+
from pycellga.optimizer import ccga
+from pycellga.individual import GeneType
+from pycellga.selection import TournamentSelection
 
 class ExampleProblem:
     def __init__(self):
@@ -428,14 +440,14 @@ 
Advanced Usage Examplesdef f(self, x):
         return sum(x)
 
-result = pycellga.optimizer.ccga(
+result = ccga(
     n_cols=5,
     n_rows=5,
     n_gen=100,
     ch_size=10,
-    gen_type=pycellga.optimizer.GeneType.BINARY,
+    gen_type=GeneType.BINARY,
     problem=ExampleProblem(),
-    selection=pycellga.optimizer.TournamentSelection,
+    selection=TournamentSelection,
     mins=[0] * 10,
     maxs=[1] * 10
 )
@@ -445,7 +457,10 @@ 
Advanced Usage Examples
 
Example 3: Machine-Coded Compact Cellular Genetic Algorithm (mcccga)

 
The Machine-Coded Compact Cellular Genetic Algorithm solves real-valued optimization problems with machine-coded representation.

-
import pycellga
+
from pycellga.optimizer import mcccga
+from pycellga.individual import GeneType
+from pycellga.selection import TournamentSelection
+
 
 class RealProblem:
     def __init__(self):
@@ -454,14 +469,14 @@ 
Advanced Usage Examplesdef f(self, x):
         return sum(np.power(xi, 2) for xi in x)
 
-result = pycellga.optimizer.mcccga(
+result = mcccga(
     n_cols=5,
     n_rows=5,
     n_gen=500,
     ch_size=5,
-    gen_type=pycellga.optimizer.GeneType.REAL,
+    gen_type=GeneType.REAL,
     problem=RealProblem(),
-    selection=pycellga.optimizer.TournamentSelection,
+    selection=TournamentSelection,
     mins=[-3.55] * 5,
     maxs=[3.55] * 5
 )
@@ -471,7 +486,11 @@ 
Advanced Usage Examples
 
Example 4: Synchronous Cellular Genetic Algorithm (sync_cga)

 
The Synchronous Cellular Genetic Algorithm optimizes a real-valued problem in a 5x5 grid using the crossover and mutation operators.

-
import pycellga
+
from pycellga.optimizer import sync_cga
+from pycellga.individual import GeneType
+from pycellga.selection import TournamentSelection
+from pycellga.recombination import BlxalphaCrossover
+from pycellga.mutation import FloatUniformMutation
 
 class ExampleProblem:
     def __init__(self):
@@ -480,18 +499,18 @@ 
Advanced Usage Examplesdef f(self, x):
         return sum(pw(xi, 2) for xi in x)
 
-result = pycellga.optimizer.sync_cga(
+result = sync_cga(
     n_cols=5,
     n_rows=5,
     n_gen=100,
     ch_size=5,
-    gen_type=pycellga.optimizer.GeneType.REAL,
+    gen_type=GeneType.REAL,
     p_crossover=0.9,
     p_mutation=0.2,
     problem=ExampleProblem(),
-    selection=pycellga.optimizer.TournamentSelection,
-    recombination=pycellga.optimizer.BlxalphaCrossover,
-    mutation=pycellga.optimizer.FloatUniformMutation,
+    selection=TournamentSelection,
+    recombination=BlxalphaCrossover,
+    mutation=FloatUniformMutation,
     mins=[-32.768] * 5,
     maxs=[32.768] * 5,
     seed_par=100
@@ -507,107 +526,114 @@ 
Customization ScenariosSelecting Gene Types

 
You can change the gen_type parameter based on the type of problem:

 
    
-
  • pycellga.optimizer.GeneType.REAL for real-valued problems.
    • 
-
  • pycellga.optimizer.GeneType.BINARY for binary problems.
    • 
-
  • pycellga.optimizer.GeneType.PERMUTATION for permutation-based problems.
    • 
+
  • pycellga.individual.GeneType.REAL for real-valued problems.
    • 
+
  • pycellga.individual.GeneType.BINARY for binary problems.
    • 
+
  • pycellga.individual.GeneType.PERMUTATION for permutation-based problems.
    • 
 

 
Problem Selection

 
pycellga includes several built-in problems for quick usage, categorized into continuous (real-valued), binary, and permutation problems. These problems allow users to directly specify the problem parameter for quick implementation.

 
Continuous Problems

 
For continuous optimization problems, the following functions are available:

 
    
-
  • pycellga.optimizer.Ackley()
    • 
-
  • pycellga.optimizer.Bentcigar()
    • 
-
  • pycellga.optimizer.Bohachevsky()
    • 
-
  • pycellga.optimizer.Chichinadze()
    • 
-
  • pycellga.optimizer.Dropwave()
    • 
-
  • pycellga.optimizer.Fms()
    • 
-
  • pycellga.optimizer.Griewank()
    • 
-
  • pycellga.optimizer.Holzman()
    • 
-
  • pycellga.optimizer.Levy()
    • 
-
  • pycellga.optimizer.Matyas()
    • 
-
  • pycellga.optimizer.Pow()
    • 
-
  • pycellga.optimizer.Powell()
    • 
-
  • pycellga.optimizer.Rastrigin()
    • 
-
  • pycellga.optimizer.Rosenbrock()
    • 
-
  • pycellga.optimizer.Rothellipsoid()
    • 
-
  • pycellga.optimizer.Schaffer()
    • 
-
  • pycellga.optimizer.Schaffer2()
    • 
-
  • pycellga.optimizer.Schwefel()
    • 
-
  • pycellga.optimizer.Sphere()
    • 
-
  • pycellga.optimizer.StyblinskiTang()
    • 
-
  • pycellga.optimizer.Sumofdifferentpowers()
    • 
-
  • pycellga.optimizer.Threehumps()
    • 
-
  • pycellga.optimizer.Zakharov()
    • 
-
  • pycellga.optimizer.Zettle()
    • 
+
  • pycellga.problems.Ackley
    • 
+
  • pycellga.problems.Bentcigar
    • 
+
  • pycellga.problems.Bohachevsky
    • 
+
  • pycellga.problems.Chichinadze
    • 
+
  • pycellga.problems.Dropwave
    • 
+
  • pycellga.problems.Fms
    • 
+
  • pycellga.problems.Griewank
    • 
+
  • pycellga.problems.Holzman
    • 
+
  • pycellga.problems.Levy
    • 
+
  • pycellga.problems.Matyas
    • 
+
  • pycellga.problems.Pow
    • 
+
  • pycellga.problems.Powell
    • 
+
  • pycellga.problems.Rastrigin
    • 
+
  • pycellga.problems.Rosenbrock
    • 
+
  • pycellga.problems.Rothellipsoid
    • 
+
  • pycellga.problems.Schaffer
    • 
+
  • pycellga.problems.Schaffer2
    • 
+
  • pycellga.problems.Schwefel
    • 
+
  • pycellga.problems.Sphere
    • 
+
  • pycellga.problems.StyblinskiTang
    • 
+
  • pycellga.problems.Sumofdifferentpowers
    • 
+
  • pycellga.problems.Threehumps
    • 
+
  • pycellga.problems.Zakharov
    • 
+
  • pycellga.problems.Zettle
    • 
 

 
Binary Problems

 
For binary optimization problems, use the following built-in options:

 
    
-
  • pycellga.optimizer.CountSat()
    • 
-
  • pycellga.optimizer.Ecc()
    • 
-
  • pycellga.optimizer.Maxcut20_01()
    • 
-
  • pycellga.optimizer.Maxcut20_09()
    • 
-
  • pycellga.optimizer.Maxcut100()
    • 
-
  • pycellga.optimizer.Mmdp()
    • 
-
  • pycellga.optimizer.OneMax()
    • 
-
  • pycellga.optimizer.Peak()
    • 
+
  • pycellga.problems.CountSat
    • 
+
  • pycellga.problems.Ecc
    • 
+
  • pycellga.problems.Maxcut20_01
    • 
+
  • pycellga.problems.Maxcut20_09
    • 
+
  • pycellga.problems.Maxcut100
    • 
+
  • pycellga.problems.Mmdp
    • 
+
  • pycellga.problems.OneMax
    • 
+
  • pycellga.problems.Peak
    • 
 

 
Permutation Problems

 
For permutation-based optimization problems, the following option is available:

 
    
-
  • pycellga.optimizer.Tsp()
    • 
+
  • pycellga.problems.Tsp
    • 
 

 
These built-in problems provide a diverse set of test cases, allowing users to explore pycellga’s capabilities across a wide range of optimization challenges. Users can also define custom problems to suit their specific needs.

 
Selection Operators

 
Choose from a variety of selection methods:

 
    
-
  • pycellga.optimizer.TournamentSelection
    • 
-
  • pycellga.optimizer.RouletteWheelSelection
    • 
+
  • pycellga.selection.TournamentSelection
    • 
+
  • pycellga.selection.RouletteWheelSelection
    • 
 

 
Recombination Operators

 
The package provides multiple crossover operators:

 
    
-
  • pycellga.optimizer.OnePointCrossover
    • 
-
  • pycellga.optimizer.PMXCrossover
    • 
-
  • pycellga.optimizer.TwoPointCrossover
    • 
-
  • pycellga.optimizer.UniformCrossover
    • 
-
  • pycellga.optimizer.ByteUniformCrossover
    • 
-
  • pycellga.optimizer.ByteOnePointCrossover
    • 
-
  • pycellga.optimizer.FlatCrossover
    • 
-
  • pycellga.optimizer.ArithmeticCrossover
    • 
-
  • pycellga.optimizer.BlxalphaCrossover
    • 
-
  • pycellga.optimizer.LinearCrossover
    • 
-
  • pycellga.optimizer.UnfairAvarageCrossover
    • 
+
  • pycellga.recombination.OnePointCrossover
    • 
+
  • pycellga.recombination.PMXCrossover
    • 
+
  • pycellga.recombination.TwoPointCrossover
    • 
+
  • pycellga.recombination.UniformCrossover
    • 
+
  • pycellga.recombination.ByteUniformCrossover
    • 
+
  • pycellga.recombination.ByteOnePointCrossover
    • 
+
  • pycellga.recombination.FlatCrossover
    • 
+
  • pycellga.recombination.ArithmeticCrossover
    • 
+
  • pycellga.recombination.BlxalphaCrossover
    • 
+
  • pycellga.recombination.LinearCrossover
    • 
+
  • pycellga.recombination.UnfairAvarageCrossover
    • 
 

 
Mutation Operators

 
You can customize mutation with these options:

 
    
-
  • pycellga.optimizer.BitFlipMutation
    • 
-
  • pycellga.optimizer.ByteMutation
    • 
-
  • pycellga.optimizer.ByteMutationRandom
    • 
-
  • pycellga.optimizer.InsertionMutation
    • 
-
  • pycellga.optimizer.ShuffleMutation
    • 
-
  • pycellga.optimizer.SwapMutation
    • 
-
  • pycellga.optimizer.TwoOptMutation
    • 
-
  • pycellga.optimizer.FloatUniformMutation
    • 
-
  • pycellga.optimizer.MutationOperator
    • 
+
  • pycellga.mutation.BitFlipMutation
    • 
+
  • pycellga.mutation.ByteMutation
    • 
+
  • pycellga.mutation.ByteMutationRandom
    • 
+
  • pycellga.mutation.InsertionMutation
    • 
+
  • pycellga.mutation.ShuffleMutation
    • 
+
  • pycellga.mutation.SwapMutation
    • 
+
  • pycellga.mutation.TwoOptMutation
    • 
+
  • pycellga.mutation.FloatUniformMutation
    • 
+
  • pycellga.mutation.MutationOperator
    • 
 

 
Example Scenarios

 
Scenario 1: Binary Optimization with Tournament Selection

 
Optimize a binary problem using tournament selection, one-point crossover, and bit-flip mutation.

-
result = pycellga.optimizer.cga(
+
from pycellga.optimizer import cga
+from pycellga.individual import GeneType
+from pycellga.problems import OneMax
+from pycellga.selection import TournamentSelection
+from pycellga.recombination import OnePointCrossover
+from pycellga.mutation import BitFlipMutation
+
+result = cga(
     n_cols=5,
     n_rows=5,
     n_gen=100,
     ch_size=10,
-    gen_type=pycellga.optimizer.GeneType.BINARY,
+    gen_type=GeneType.BINARY,
     p_crossover=0.8,
     p_mutation=0.1,
-    problem=pycellga.optimizer.OneMax(),  # Built-in binary optimization problem
-    selection=pycellga.optimizer.TournamentSelection,
-    recombination=pycellga.optimizer.OnePointCrossover,
-    mutation=pycellga.optimizer.BitFlipMutation,
+    problem=OneMax(),  # Built-in binary optimization problem
+    selection=TournamentSelection,
+    recombination=OnePointCrossover,
+    mutation=BitFlipMutation,
     mins=[0] * 10,
     maxs=[1] * 10,
     seed_par=100
@@ -618,18 +644,25 @@ 
Customization Scenarios
 
Scenario 2: Real-Valued Optimization with Byte One Point Crossover

 
Solve a real-valued optimization problem using Byte One Point Crossover and Byte Mutation.

-
result = pycellga.optimizer.cga(
+
from pycellga.optimizer import cga
+from pycellga.individual import GeneType
+from pycellga.problems import Ackley
+from pycellga.selection import RouletteWheelSelection
+from pycellga.recombination import ByteOnePointCrossover
+from pycellga.mutation import ByteMutation
+
+result = cga(
     n_cols=5,
     n_rows=5,
     n_gen=100,
     ch_size=10,
-    gen_type=pycellga.optimizer.GeneType.REAL,
+    gen_type=GeneType.REAL,
     p_crossover=0.9,
     p_mutation=0.2,
-    problem=pycellga.optimizer.Ackley(10),  # Built-in real-valued optimization problem
-    selection=pycellga.optimizer.RouletteWheelSelection,
-    recombination=pycellga.optimizer.ByteOnePointCrossover,
-    mutation=pycellga.optimizer.ByteMutation,
+    problem=Ackley(10),  # Built-in real-valued optimization problem
+    selection=RouletteWheelSelection,
+    recombination=ByteOnePointCrossover,
+    mutation=ByteMutation,
     mins=[-32.768] * 10,
     maxs=[32.768] * 10,
     seed_par=100
@@ -640,18 +673,25 @@ 
Customization Scenarios
 
Scenario 3: Permutation Optimization for Traveling Salesman Problem

 
Optimize a TSP using permutation encoding, PMX crossover, and swap mutation.

-
result = pycellga.optimizer.cga(
+
from pycellga.optimizer import cga
+from pycellga.individual import GeneType
+from pycellga.problems import Tsp
+from pycellga.selection import TournamentSelection
+from pycellga.recombination import PMXCrossover
+from pycellga.mutation import SwapMutation
+
+result = cga(
     n_cols=5,
     n_rows=5,
     n_gen=300,
     ch_size=14,  # Number of cities
-    gen_type=pycellga.optimizer.GeneType.PERMUTATION,
+    gen_type=GeneType.PERMUTATION,
     p_crossover=0.85,
     p_mutation=0.15,
-    problem=pycellga.optimizer.Tsp(),  # Built-in TSP optimization problem
-    selection=pycellga.optimizer.TournamentSelection,
-    recombination=pycellga.optimizer.PMXCrossover,
-    mutation=pycellga.optimizer.SwapMutation,
+    problem=Tsp(),  # Built-in TSP optimization problem
+    selection=TournamentSelection,
+    recombination=PMXCrossover,
+    mutation=SwapMutation,
     mins=[1] * 14,
     maxs=[14] * 14,
     seed_par=100