Fixed method names and added some crossover methods and tests for floats

src/EasyGA.py

@@ -6,22 +6,23 @@ from structure import Chromosome as create_chromosome
 from structure import Gene as create_gene
 
 # Structure Methods
-from fitness_function import Fitness_Examples
-from initialization import Initialization_Methods
+from fitness_function  import Fitness_Examples
+from initialization    import Initialization_Methods
 from termination_point import Termination_Methods
 
 # Parent/Survivor Selection Methods
-from parent_selection import Parent_Selection
+from parent_selection   import Parent_Selection
 from survivor_selection import Survivor_Selection
 
 # Genetic Operator Methods
-from mutation import Mutation_Methods
+from mutation  import Mutation_Methods
 from crossover import Crossover_Methods
 
 class GA:
 
     def __init__(self):
         """Initialize the GA."""
+
         # Initilization variables
         self.chromosome_length   = 10
         self.population_size     = 10
@@ -33,7 +34,7 @@ class GA:
 
         # Selection variables
         self.parent_ratio          = 0.1
-        self.selection_probability = 0.95
+        self.selection_probability = 0.75
         self.tournament_size_ratio = 0.1
 
         # Termination variables
@@ -54,7 +55,7 @@ class GA:
 
         # Methods for accomplishing Parent-Selection -> Crossover -> Survivor_Selection -> Mutation
         self.parent_selection_impl     = Parent_Selection.Tournament.with_replacement
-        self.crossover_individual_impl = Crossover_Methods.Individual.single_point_crossover
+        self.crossover_individual_impl = Crossover_Methods.Individual.single_point
         self.crossover_population_impl = Crossover_Methods.Population.random_selection
         self.survivor_selection_impl   = Survivor_Selection.fill_in_best
         self.mutation_individual_impl  = Mutation_Methods.Individual.single_gene
@@ -82,7 +83,7 @@ class GA:
             else:
                 self.population.reset_mating_pool()
                 self.set_all_fitness()
-                self.population.set_all_chromosomes(self.sort_by_best_fitness(self.population.get_all_chromosomes()))
+                self.population.sort_by_best_fitness(self)
                 self.parent_selection_impl(self)
                 next_population = self.crossover_population_impl(self)
                 self.survivor_selection_impl(self, next_population)
@@ -119,7 +120,7 @@ class GA:
         """
 
         # Check each chromosome
-        for chromosome in self.population.get_all_chromosomes():
+        for chromosome in self.population.get_chromosome_list():
 
             # Update fitness if needed or asked by the user
             if(chromosome.get_fitness() is None or self.update_fitness):

src/crossover/crossover_methods.py

@@ -27,13 +27,56 @@ class Crossover_Methods:
     class Individual:
         """Methods for crossing parents"""
 
-        def single_point_crossover(ga, parent_one, parent_two):
+        def single_point(ga, parent_one, parent_two):
             """Cross two parents by swapping genes at one random point"""
 
             index = random.randint(0, parent_one.size()-1)
             return ga.make_chromosome(parent_one.get_gene_list()[:index] + parent_two.get_gene_list()[index:])
 
 
-        def multi_point_crossover(ga, parent_one, parent_two):
+        def multi_point(ga, parent_one, parent_two):
             """Cross two parents by swapping genes at multiple points"""
             pass
+
+
+        def uniform(ga, parent_one, parent_two):
+            """Cross two parents by swapping all genes randomly"""
+            return ga.make_chromosome([
+                       random.choice([parent_one.get_gene(i), parent_two.get_gene(i)])
+                   for i in range(parent_one.size())])
+
+        class Arithmetic:
+            """Crossover methods for numerical genes"""
+
+            def int_random(ga, parent_one, parent_two):
+                """Cross two parents by taking a random integer value between each of the genes"""
+                return ga.make_chromosome([
+                           ga.make_gene(random.randint(*sorted([parent_one.get_gene(i).get_value(), parent_two.get_gene(i).get_value()])))
+                       for i in range(parent_one.size())])
+
+
+            def int_weighted(ga, parent_one, parent_two):
+                """Cross two parents by taking a a weighted average of the genes"""
+
+                # the percentage of genes taken from the first gene
+                weight = 0.25
+                return ga.make_chromosome([
+                           ga.make_gene(int(weight*parent_one.get_gene(i).get_value()+(1-weight)*parent_two.get_gene(i).get_value()))
+                       for i in range(parent_one.size())])
+
+
+            def float_random(ga, parent_one, parent_two):
+                """Cross two parents by taking a random numeric value between each of the genes"""
+                return ga.make_chromosome([
+                           ga.make_gene(random.uniform(parent_one.get_gene(i).get_value(), parent_two.get_gene(i).get_value()))
+                       for i in range(parent_one.size())])
+
+
+            def float_weighted(ga, parent_one, parent_two):
+                """Cross two parents by taking a a weighted average of the genes"""
+
+                # the percentage of genes taken from the first gene
+                weight = 0.25
+                return ga.make_chromosome([
+                           ga.make_gene(weight*parent_one.get_gene(i).get_value()+(1-weight)*parent_two.get_gene(i).get_value())
+                       for i in range(parent_one.size())])

src/fitness_function/fitness_examples.py

@@ -17,6 +17,13 @@ class Fitness_Examples:
         return fitness
 
 
+    def near_5(chromosome):
+        """Test's the GA's ability to handle floats.
+        Computes how close each gene is to 5.
+        """
+        return sum([1-pow(1-gene.get_value()/5, 2) for gene in chromosome.get_gene_list()])
+
+
     def index_dependent_values(chromosome):
         """Test of the GA's ability to improve fitness when the value is index-dependent.
         If a gene is equal to its index in the chromosome + 1, fitness is incremented.

src/run_testing.py

@@ -3,15 +3,19 @@ import EasyGA
 
 # Create the Genetic algorithm
 ga = EasyGA.GA()
-ga.population_size = 100
-ga.generation_goal = 200
+ga.population_size = 25
+ga.generation_goal = 100
+ga.gene_impl       = [random.randint,0,10]
+ga.selection_probability = 0.5
+ga.fitness_function_impl     = EasyGA.Fitness_Examples.near_5
 ga.parent_selection_impl     = EasyGA.Parent_Selection.Roulette.stochastic_selection
 ga.crossover_population_impl = EasyGA.Crossover_Methods.Population.sequential_selection
-ga.survivor_selection_impl   = EasyGA.Survivor_Selection.fill_in_parents_then_random
+ga.crossover_individual_impl = EasyGA.Crossover_Methods.Individual.Arithmetic.int_random
+ga.survivor_selection_impl   = EasyGA.Survivor_Selection.fill_in_best
 
 ga.evolve()
 ga.set_all_fitness()
-ga.population.set_all_chromosomes(ga.sort_by_best_fitness(ga.population.get_all_chromosomes()))
+ga.population.sort_by_best_fitness(ga)
 
 print(f"Current Generation: {ga.current_generation}")
 ga.population.print_all()

src/structure/chromosome.py

@@ -7,8 +7,6 @@ class Chromosome:
             self.gene_list = gene_list
 
         self.fitness = None
-        # If the chromosome has been selected then the flag would switch to true
-        self.selected = False
 
 
     def size(self):
@@ -16,10 +14,10 @@ class Chromosome:
         return len(self.gene_list)
 
 
-    def add_gene(self, gene, index = -1):
+    def add_gene(self, gene, index = None):
         """Add a gene to the chromosome at the specified index, defaulted to end of the chromosome"""
-        if index == -1:
-            index = len(self.gene_list)
+        if index is None:
+            index = self.size()
         self.gene_list.insert(index, gene)
 
 
@@ -30,7 +28,7 @@ class Chromosome:
 
     def get_gene(self, index):
         """Returns the gene at the given index"""
-        return gene_list[index]
+        return self.gene_list[index]
 
 
     def get_gene_list(self):

src/structure/population.py

@@ -14,7 +14,7 @@ class Population:
 
     def sort_by_best_fitness(self, ga):
         """Sorts the population by fitness"""
-        self.set_all_chromosomes(ga.sort_by_best_fitness(self.chromosome_list))
+        self.set_chromosome_list(ga.sort_by_best_fitness(self.chromosome_list))
 
 
     def size(self):
@@ -27,10 +27,10 @@ class Population:
         pass
 
 
-    def add_chromosome(self, chromosome, index = -1):
+    def add_chromosome(self, chromosome, index = None):
         """Adds a chromosome to the population at the input index, defaulted to the end of the chromosome set"""
-        if index == -1:
-            index = len(self.chromosome_list)
+        if index is None:
+            index = self.size()
         self.chromosome_list.insert(index, chromosome)
 
 
@@ -64,7 +64,7 @@ class Population:
         return self.mating_pool[index]
 
 
-    def get_all_chromosomes(self):
+    def get_chromosome_list(self):
         """Returns all chromosomes in the population"""
         return self.chromosome_list
 
@@ -80,11 +80,11 @@ class Population:
 
 
     def set_parent(self, index):
-        """Sets the index chromosome from the population as a parent"""
+        """Sets the indexed chromosome from the population as a parent"""
         self.add_parent(self.get_chromosome(index))
 
 
-    def set_all_chromosomes(self, chromosome_list):
+    def set_chromosome_list(self, chromosome_list):
         """Sets the chromosome list"""
         self.chromosome_list = chromosome_list
 
@@ -120,5 +120,5 @@ class Population:
         print("Current population:")
 
         for index in range(self.size()):
-            print(f'Chromosome - {index} {self.chromosome_list[index]}', end = "")
-            print(f' / Fitness = {self.chromosome_list[index].get_fitness()}')
+            print(f'Chromosome - {index} {self.get_chromosome(index)}', end = "")
+            print(f' / Fitness = {self.get_chromosome(index).get_fitness()}')

src/survivor_selection/survivor_selection_methods.py

@@ -6,22 +6,22 @@ class Survivor_Selection:
     def fill_in_best(ga, next_population):
         """Fills in the next population with the best chromosomes from the last population"""
 
-        ga.population.set_all_chromosomes(ga.population.get_all_chromosomes()[:ga.population.size()-next_population.size()] + next_population.get_all_chromosomes())
+        ga.population.set_chromosome_list(ga.population.get_chromosome_list()[:ga.population.size()-next_population.size()] + next_population.get_chromosome_list())
 
 
     def fill_in_random(ga, next_population):
         """Fills in the next population with random chromosomes from the last population"""
 
-        ga.population.set_all_chromosomes([
-                              random.choice(ga.population.get_all_chromosomes())
+        ga.population.set_chromosome_list([
+                              random.choice(ga.population.get_chromosome_list())
                           for n in range(ga.population.size()-next_population.size())]
-                      + next_population.get_all_chromosomes())
+                      + next_population.get_chromosome_list())
 
 
     def fill_in_parents_then_random(ga, next_population):
         """Fills in the next population with all parents followed by random chromosomes from the last population"""
 
-        ga.population.set_all_chromosomes([
-                              random.choice(ga.population.get_all_chromosomes())
+        ga.population.set_chromosome_list([
+                              random.choice(ga.population.get_chromosome_list())
                           for n in range(ga.population.size()-len(ga.population.get_mating_pool())-next_population.size())]
-                      + ga.population.get_mating_pool() + next_population.get_all_chromosomes())
+                      + ga.population.get_mating_pool() + next_population.get_chromosome_list())