File name changes to match the rest of framework. generation goal was setup twice in EasyGA.

src/EasyGA.py

@@ -32,14 +32,13 @@ class GA:
 
         # Termination variables
         self.current_generation = 0
-        self.generation_goal = 50
-
         self.current_fitness = 0
+
         self.generation_goal = 250
         self.fitness_goal = 9
 
         # Mutation variables
-        self.mutation_rate = 0.10        
+        self.mutation_rate = 0.10
 
         # Default EasyGA implimentation structure
         self.initialization_impl = Initialization_Methods.random_initialization
@@ -52,7 +51,7 @@ class GA:
         self.mutation_impl = Mutation_Methods.per_gene_mutation
         # The type of termination to impliment
         self.termination_impl = Termination_Methods.generation_based
-        
+
 
     def evolve_generation(self, number_of_generations = 1, consider_termination = True):
         """Evolves the ga the specified number of generations."""
@@ -121,7 +120,7 @@ class GA:
                 else:
                     not_sorted_check += 1
 
-        chromosome_set = chromosome_set_temp 
+        chromosome_set = chromosome_set_temp
 
         return chromosome_set
 

src/crossover/crossover_methods.py

@@ -9,13 +9,13 @@ class Crossover_Methods:
     """
     def __init__(self):
         pass
-    
+
     def single_point_crossover(ga):
         """Single  point crossover is when a "point" is selected and the genetic
         make up of the two parent chromosomes are "Crossed" or better known as swapped"""
 
         crossover_pool = ga.population.mating_pool
-        
+
         new_population = Population()
         for i in range(len(crossover_pool)):
             if i + 1 < len(crossover_pool):
@@ -23,14 +23,14 @@ class Crossover_Methods:
                 parent_one = crossover_pool[i].get_genes()
                 parent_two = crossover_pool[i+1].get_genes()
                 #halfway_point = int(ga.chromosome_length/2)
-                split_point = random.randint(0,ga.chromosome_length)    
+                split_point = random.randint(0,ga.chromosome_length)
                 new_gene_set.extend(parent_one[0:split_point])
                 new_gene_set.extend(parent_two[split_point:])
                 new_chromosome = Chromosome(new_gene_set)
                 new_population.add_chromosome(new_chromosome)
 
         return new_population
-    
+
     def multi_point_crossover(ga, number_of_points = 2):
         """Multi point crossover is when a specific number (More then one) of
         "points" are created to merge the genetic makup of the chromosomes."""

src/crossover/methods.py

@@ -1,3 +0,0 @@
-class Crossover_methods:
-    """Mutation examples will go here """
-    pass

src/fitness_function/methods.py

@@ -1,16 +0,0 @@
-class Fitness_methods:
-    """Fitness function examples used"""
-
-    def is_it_5(chromosome):
-        """A very simple case test function - If the chromosomes gene value is a 5 add one
-         to the chromosomes overall fitness value."""
-        # Overall fitness value
-        fitness = 0
-        # For each gene in the chromosome
-        for gene in chromosome.gene_list:
-            # Check if its value = 5
-            if(gene.value == 5):
-                # If its value is 5 then add one to
-                # the overal fitness of the chromosome.
-                fitness += 1
-        return fitness

src/fitness_function/test_examples.py

@@ -1,12 +0,0 @@
-class test_fitness_funciton:
-    def get_fitness(self, chromosome):
-        # For every gene in chromosome
-        for i in range(len(chromosome.genes)):
-            # If the gene has a five then add one to the fitness
-            # Example -> Chromosome = [5],[2],[2],[5],[5] then fitness = 3
-            if (chromosome.genes[i].get_value == 5):
-                # Add to the genes fitness
-                chromosome.genes[i].fitness += 1
-                # Add to the chromosomes fitness
-                chromosome.fitness += 1
-        return chromosome.fitness

src/mutation/methods.py

@@ -1,3 +0,0 @@
-class Mutation_methods:
-    """Mutation examples will go here """
-    pass

src/parent_selection/__init__.py

@@ -1,2 +1,2 @@
 # FROM (. means local) file_name IMPORT function_name
-from .parent_selection import Parent_Selection
+from .parent_selection_methods import Parent_Selection

src/run_testing.py

@@ -13,4 +13,5 @@ ga.gene_impl = [random.randrange,1,100]
 ga.evolve()
 
 # Print the current population
+print(f"Current Generation: {ga.current_generation}")
 ga.population.print_all()

src/survivor_selection/__init__.py

@@ -1,2 +1,2 @@
 # FROM (. means local) file_name IMPORT function_name
-from .survivor_selection import Survivor_Selection
+from .survivor_selection_methods import Survivor_Selection

src/termination_point/methods.py

@@ -1,16 +0,0 @@
-class Termination_methods:
-    """Example functions that can be used to terminate the the algorithms loop"""
-
-    def fitness_based(ga):
-        """Fitness based approach to terminate when the goal fitness has been reached"""
-        status = True
-        if(ga.current_fitness > ga.fitness_goal):
-            status = False
-        return status
-
-    def generation_based(ga):
-        """Generation based approach to terminate when the goal generation has been reached"""
-        status = True
-        if(ga.current_generation > ga.generation_goal):
-            status = False
-        return status