Added blank lines and fixed run_testing

src/EasyGA.py

@@ -1,20 +1,25 @@
 import random
+
 # Import all the data structure prebuilt modules
 from initialization import Population as create_population
 from initialization import Chromosome as create_chromosome
 from initialization import Gene as create_gene
+
 # Structure Methods
 from fitness_function import Fitness_Examples
 from initialization import Initialization_Methods
 from termination_point import Termination_Methods
+
 # Population Methods
 from survivor_selection import Survivor_Selection
 from parent_selection import Parent_Selection
+
 # Manipulation Methods
 from mutation import Mutation_Methods
 from crossover import Crossover_Methods
 
 class GA:
+
     def __init__(self):
         """Initialize the GA."""
         # Initilization variables
@@ -44,12 +49,15 @@ class GA:
         # Default EasyGA implimentation structure
         self.initialization_impl = Initialization_Methods.random_initialization
         self.fitness_function_impl = Fitness_Examples.index_dependent_values
+
         # Selects which chromosomes should be automaticly moved to the next population
         self.survivor_selection_impl = Survivor_Selection.fill_in_best
+
         # Methods for accomplishing parent-selection -> Crossover -> Mutation
         self.parent_selection_impl = Parent_Selection.Tournament.with_replacement
         self.crossover_impl = Crossover_Methods.single_point_crossover
         self.mutation_impl = Mutation_Methods.per_gene_mutation
+
         # The type of termination to impliment
         self.termination_impl = Termination_Methods.generation_based
 
@@ -76,22 +84,26 @@ class GA:
 
             self.current_generation += 1
 
+
     def evolve(self):
         """Runs the ga until the termination point has been satisfied."""
         # While the termination point hasnt been reached keep running
         while(self.active()):
             self.evolve_generation()
 
+
     def active(self):
         """Returns if the ga should terminate base on the termination implimented"""
         # Send termination_impl the whole ga class
         return self.termination_impl(self)
 
+
     def initialize_population(self):
         """Initialize the population using the initialization
          implimentation that is currently set"""
         self.population = self.initialization_impl(self)
 
+
     def set_all_fitness(self,chromosome_set):
         """Will get and set the fitness of each chromosome in the population.
         If update_fitness is set then all fitness values are updated.
@@ -104,6 +116,7 @@ class GA:
                 # Set the chromosomes fitness using the fitness function
                 chromosome.set_fitness(self.fitness_function_impl(chromosome))
 
+
     def sort_by_best_fitness(self, chromosome_set):
 
         chromosome_set_temp = chromosome_set
@@ -120,14 +133,17 @@ class GA:
 
         return chromosome_set
 
+
     def make_gene(self,value):
         """Let's the user create a gene."""
         return create_gene(value)
 
+
     def make_chromosome(self):
         """Let's the user create a chromosome."""
         return create_chromosome()
 
+
     def make_population(self):
         """Let's the user create a population."""
         return create_population()

src/fitness_function/fitness_examples.py

@@ -1,5 +1,6 @@
 class Fitness_Examples:
     """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."""
@@ -15,6 +16,7 @@ class Fitness_Examples:
                 
         return fitness
 
+
     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/initialization/chromosome_structure/chromosome.py

@@ -1,4 +1,5 @@
 class Chromosome:
+
     def __init__(self, gene_list = None):
         if gene_list is None:
             self.gene_list = []
@@ -9,36 +10,45 @@ class Chromosome:
         # If the chromosome has been selected then the flag would switch to true
         self.selected = False
 
+
     def size(self):
         """Returns the number of genes in the chromosome"""
         return len(self.gene_list)
 
+
     def add_gene(self, gene, index = -1):
         """Add a gene to the chromosome at the specified index, defaulted to end of the chromosome"""
         if index == -1:
             index = len(self.gene_list)
         self.gene_list.insert(index, gene)
 
+
     def remove_gene(self, index):
         del self.gene_list[index]
 
+
     def get_genes(self):
         return self.gene_list
 
+
     def get_fitness(self):
         """Return the fitness of the chromosome"""
         return self.fitness
 
+
     def set_gene(self, gene, index):
         self.gene_list[index] = gene
 
+
     def set_genes(self, genes):
         self.gene_list = genes
 
+
     def set_fitness(self, fitness):
         """Set the fitness value of the chromosome"""
         self.fitness = fitness
 
+
     def __repr__(self):
         """Format the repr() output for the chromosome"""
         output_str = ''

src/initialization/gene_structure/gene.py

@@ -3,6 +3,7 @@ def check_gene(value):
     assert value != "" , "Gene can not be empty"
     return value
 
+
 class Gene:
 
     def __init__(self, value):
@@ -10,22 +11,27 @@ class Gene:
         self.fitness = None
         self.value = check_gene(value)
 
+
     def get_fitness(self):
         """Return fitness of the gene"""
         return self.fitness
 
+
     def get_value(self):
         """Return value of the gene"""
         return self.value
 
+
     def set_fitness(self, fitness):
         """Set fitness of the gene"""
         self.fitness = fitness
 
+
     def set_value(self, value):
         """Set value of the gene"""
         self.value = value
 
+
     def __repr__(self):
         """Format the repr() output value"""
         return f'[{self.value}]'

src/initialization/initialization_methods.py

@@ -28,5 +28,6 @@ class Initialization_Methods:
                 else:
                     #Exit because either were not specified
                     print("You did not specify any initialization constraints.")
+                    break
             population.add_chromosome(chromosome)
         return population

src/initialization/population_structure/population.py

@@ -1,60 +1,79 @@
 class Population:
+
     def __init__(self, chromosome_list = None):
         """Intiialize the population with fitness of value None, and a set of chromosomes dependant on user-passed parameter"""
+
         if chromosome_list is None:
           self.chromosome_list = []
         else:
           self.chromosome_list = chromosome_list
+
         self.fitness = None
         self.mating_pool = []
 
+
     def size(self):
         """Returns the size of the population"""
         return len(self.chromosome_list)
 
+
     def get_closet_fitness(self,value):
         """Get the chomosome that has the closets fitness to the value defined"""
         pass
 
+
     def add_chromosome(self, chromosome, index = -1):
         """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)
         self.chromosome_list.insert(index, chromosome)
 
+
     def remove_chromosome(self, index):
         """removes a chromosome from the indicated index"""
         del self.chromosome_list[index]
 
+
     def get_all_chromosomes(self):
         """returns all chromosomes in the population"""
         return self.chromosome_list
 
+
     def get_fitness(self):
         """returns the population's fitness"""
         return self.fitness
 
+
     def set_all_chromosomes(self, chromosomes):
         self.chromosome_list = chromosomes
 
+
     def set_chromosome(self, chromosome, index = -1):
         if index == -1:
             index = len(self.chromosomes)-1
         self.chromosome_list[index] = chromosome
 
+
     def set_fitness(self, fitness):
         """Sets the fitness value of the population"""
         self.fitness = fitness
 
+
     def __repr__(self):
-        for index in range(len(self.chromosomes)):
+        """Returns a string representation of the entire population"""
-            return f'{self.chromosome_list[index]}'
+        pass
+
 
     def print_all(self):
-        """Prints information about the population in the following format:"""
+        """Prints information about the population in the following format:
-        """Ex .Current population"""
+        Current population
-        """Chromosome 1 - [gene][gene][gene][.etc] /  Chromosome fitness - """
+        Chromosome 1 - [gene][gene][gene][.etc] / Chromosome fitness -
+        Chromosome 2 - [gene][gene][gene][.etc] / Chromosome fitness -
+        etc.
+        """
+
         print("Current population:")
-        for index in range(len(self.chromosome_list)):
+
+        for index in range(self.size()):
             print(f'Chromosome - {index} {self.chromosome_list[index]}', end = "")
-            print(f' / Fitness = {self.chromosome_list[index].fitness}')
+            print(f' / Fitness = {self.chromosome_list[index].get_fitness()}')

src/parent_selection/parent_selection_methods.py

@@ -5,7 +5,9 @@ from initialization.population_structure.population import Population
 from initialization.chromosome_structure.chromosome import Chromosome
 
 class Parent_Selection:
+
     class Tournament:
+
         def with_replacement(ga):
             """
             Will make tournaments of size tournament_size and choose the winner (best fitness) from the tournament and use it as a parent for the next generation
@@ -33,7 +35,9 @@ class Parent_Selection:
                     if random.uniform(0, 1) < selection_probability * pow(1-selection_probability, index):
                         ga.population.mating_pool.append(tournament_group[index])
 
+
     class Roulette:
+
         def roulette_selection(ga):
             """Roulette selection works based off of how strong the fitness is of the
             chromosomes in the population. The stronger the fitness the higher the probability
@@ -41,6 +45,7 @@ class Parent_Selection:
             Where the chromosomes are the numbers to be selected and the board size for
             those numbers are directly proportional to the chromosome's current fitness. Where
             the ball falls is a randomly generated number between 0 and 1"""
+            
             total_fitness = sum(ga.population.chromosome_list[i].get_fitness() for i in range(ga.population.size()))
             rel_fitnesses = []
             

src/run_testing.py

@@ -1,5 +1,5 @@
-import EasyGA
 import random
+import EasyGA
 
 # Create the Genetic algorithm
 ga = EasyGA.GA()
@@ -8,6 +8,7 @@ ga.population_size = 100
 ga.chromosome_length = 10
 ga.generation_goal =  100
 ga.gene_impl = [random.randint,1,10]
+ga.parent_selection_impl = EasyGA.Parent_Selection.Roulette.roulette_selection
 
 ga.evolve()
 

src/termination_point/termination_methods.py

@@ -4,13 +4,21 @@ class Termination_Methods:
     def fitness_based(ga):
         """Fitness based approach to terminate when the goal fitness has been reached"""
 
+        # Need to start the algorithm if the population is None
         if ga.population == None:
             return True
-        for i in range(ga.population.size()):
+
-            if(ga.population.get_all_chromosomes()[i].fitness >= ga.fitness_goal):
+        # Check all chromosomes
+        for chromosome in ga.population.get_all_chromosomes():
+
+            # Stop if a chromosome has reached the fitness_goal
+            if(chromosome.fitness >= ga.fitness_goal):
                 return False
+
+        # Continue if no chromosomes have reached the fitness goal
         return True
 
+
     def generation_based(ga):
         """Generation based approach to terminate when the goal generation has been reached"""