Removing problem info from Individual

src/individual.py

@@ -38,7 +38,6 @@ class Individual:
     def __init__(self, 
                  gen_type: GeneType = GeneType.BINARY, 
                  ch_size: int = 0,
-                 problem: AbstractProblem = None,
                  mins : list[float] = [],
                  maxs : list[float] = []):
         """
@@ -47,7 +46,7 @@ def __init__(self,
         Parameters
         ----------
         gen_type : str, optional
-            The type of genome representation. Must be one of GeneType.BINARY, "Permutation", or "Real". (default is GeneType.BINARY)
+            The type of genome representation. Must be one of GeneType.BINARY, GeneType.PERMUTATION, or GeneType.REAL. (default is GeneType.BINARY)
         ch_size : int
             The size of the chromosome.
         mins: list[float]
@@ -57,7 +56,6 @@ def __init__(self,
         """
         self.gen_type = gen_type
         self.ch_size = ch_size
-        self.problem = problem
         self.chromosome = []
         self.fitness_value = 0
         self.position = (0, 0)

src/optimizer.py

@@ -359,8 +359,8 @@ def alpha_cga(
         Number of generations to run the optimization.
     ch_size : int
         Size of the chromosome.
-    gen_type : str
-        Type of genome representation (GeneType.BINARY, "Permutation", "Real").
+    gen_type : GeneType
+        Type of genome representation (GeneType.BINARY, GeneType.PERMUTATION, or GeneType.REAL).
     p_crossover : float
         Probability of crossover, should be between 0 and 1.
     p_mutation : float
@@ -521,8 +521,8 @@ def ccga(
         Number of generations to run the optimization.
     ch_size : int
         Size of the chromosome.
-    gen_type : str
-        Type of genome representation (GeneType.BINARY, "Permutation", "Real").
+    gen_type : GeneType
+        Type of genome representation (GeneType.BINARY, Genetype.PERMUTATION, GeneType.REAL).
     problem : AbstractProblem
         The problem instance used to evaluate fitness.
     selection : Callable

src/population.py

@@ -1,12 +1,8 @@
 from typing import List
 from individual import *
 from grid import *
-from neighborhoods.linear_5 import Linear5
 from neighborhoods.linear_9 import Linear9
-from neighborhoods.compact_9 import Compact9
-from neighborhoods.compact_13 import Compact13
-from neighborhoods.compact_21 import Compact21
-from neighborhoods.compact_25 import Compact25
+
 from byte_operators import *
 
 from problems.abstract_problem import AbstractProblem
@@ -90,7 +86,7 @@ def initial_population(self) -> List[Individual]:
 
         for i in range(pop_size):
             ind = Individual(gen_type = self.gen_type, ch_size = self.ch_size, 
-                             problem = self.problem, mins = self.mins, maxs = self.maxs)
+                             mins = self.mins, maxs = self.maxs)
 
             # Initialize chromosome and evaluate fitness for cga, syn_cga and alpha_cga
             if self.method_name in ["cga", "sync_cga", "alpha_cga", "ccga"]:

src/tests/test_individual.py

@@ -2,23 +2,20 @@
 from numpy import random
 import random as rd
 from individual import Individual, GeneType 
-from problems.single_objective.discrete.binary.one_max import OneMax
-from problems.single_objective.continuous.ackley import Ackley
-from problems.single_objective.discrete.permutation.tsp import Tsp
 
 
 @pytest.fixture
 def setup_individual():
     """
     Fixture to provide an instance of the Individual class with different configurations.
     """
-    return Individual(gen_type=GeneType.BINARY, ch_size=10, problem=OneMax())
+    return Individual(gen_type=GeneType.BINARY, ch_size=10)
 
 def test_individual_init():
     """
     Test the initialization of the Individual class.
     """
-    ind = Individual(gen_type=GeneType.BINARY, ch_size=10, problem=OneMax())
+    ind = Individual(gen_type=GeneType.BINARY, ch_size=10)
     assert ind.gen_type == GeneType.BINARY
     assert ind.ch_size == 10
     assert ind.chromosome == []
@@ -31,7 +28,7 @@ def test_randomize_binary():
     """
     Test the randomization of the chromosome for a binary genome type.
     """
-    ind = Individual(gen_type=GeneType.BINARY, ch_size=10, problem=OneMax())
+    ind = Individual(gen_type=GeneType.BINARY, ch_size=10)
     ind.randomize()
     assert len(ind.chromosome) == 10
     assert all(gene in [0, 1] for gene in ind.chromosome)
@@ -41,7 +38,7 @@ def test_randomize_permutation():
     Test the randomization of the chromosome for a permutation genome type.
     """
     chsize = 14
-    ind = Individual(gen_type=GeneType.PERMUTATION, ch_size=chsize, problem=Tsp())
+    ind = Individual(gen_type=GeneType.PERMUTATION, ch_size=chsize)
     ind.randomize()
     assert len(ind.chromosome) == chsize
     for i in range(1, chsize+1):
@@ -54,7 +51,7 @@ def test_randomize_real_valued():
     Test the randomization of the chromosome for a real-valued genome type.
     """
     chsize = 10
-    ind = Individual(gen_type=GeneType.REAL, ch_size=chsize, problem=Ackley())
+    ind = Individual(gen_type=GeneType.REAL, ch_size=chsize)
     ind.randomize()
     assert len(ind.chromosome) == chsize
     assert all(isinstance(gene, float) for gene in ind.chromosome)
@@ -64,7 +61,7 @@ def test_illegal_genome_type():
     Test that an exception is raised when an illegal genome type is provided.
     """
     try:
-        ind = Individual(gen_type="Illegal genome type", ch_size=10, problem=OneMax())
+        ind = Individual(gen_type="Illegal genome type", ch_size=10)
     except Exception:
         # Passes the test if an exception is raised
         assert True

src/tests/test_one_point_crossover.py

@@ -1,6 +1,6 @@
-from problems.single_objective.discrete.binary.one_max import OneMax
 from recombination.one_point_crossover import OnePointCrossover
 from individual import Individual, GeneType
+from problems.single_objective.discrete.binary.one_max import OneMax
 
 def test_one_point_crossover():
     """
@@ -23,8 +23,8 @@ def test_one_point_crossover():
     CHSIZE = 10
 
     # Create two parent individuals with binary chromosomes of the specified size
-    indv1 = Individual(gen_type=GeneType.BINARY, ch_size=CHSIZE, problem=OneMax())
-    indv2 = Individual(gen_type=GeneType.BINARY, ch_size=CHSIZE, problem=OneMax())
+    indv1 = Individual(gen_type=GeneType.BINARY, ch_size=CHSIZE)
+    indv2 = Individual(gen_type=GeneType.BINARY, ch_size=CHSIZE)
 
     # Randomize the chromosomes of the parents
     indv1.randomize()
@@ -33,8 +33,7 @@ def test_one_point_crossover():
     parents = [indv1, indv2]
 
     # Initialize the OnePointCrossover with the parent individuals and problem
-    theproblem = OneMax()
-    ucx = OnePointCrossover(parents, theproblem)
+    ucx = OnePointCrossover(parents, OneMax())
 
     # Perform the crossover to get two offspring
     child1, child2 = ucx.get_recombinations()

src/tests/test_two_point_crossover.py

@@ -22,8 +22,8 @@ def test_two_point_crossover():
     CHSIZE = 10
 
     # Create two parent individuals with binary chromosomes of the specified size
-    indv1 = Individual(gen_type=GeneType.BINARY, ch_size=CHSIZE, problem=OneMax())
-    indv2 = Individual(gen_type=GeneType.BINARY, ch_size=CHSIZE, problem=OneMax())
+    indv1 = Individual(gen_type=GeneType.BINARY, ch_size=CHSIZE)
+    indv2 = Individual(gen_type=GeneType.BINARY, ch_size=CHSIZE)
 
     # Randomly initialize the chromosomes of the parents
     indv1.randomize()

src/tests/test_uniform_crossover.py

@@ -21,8 +21,8 @@ def test_uniform_crossover():
     CHSIZE = 10
 
     # Create two parent individuals with binary chromosomes of the specified size
-    indv1 = Individual(gen_type=GeneType.BINARY, ch_size=CHSIZE, problem=OneMax())
-    indv2 = Individual(gen_type=GeneType.BINARY, ch_size=CHSIZE, problem=OneMax())
+    indv1 = Individual(gen_type=GeneType.BINARY, ch_size=CHSIZE)
+    indv2 = Individual(gen_type=GeneType.BINARY, ch_size=CHSIZE)
 
     # Randomly initialize the chromosomes of the parents
     indv1.randomize()