Initialization for GA with arguments

src/EasyGA.py

@@ -22,8 +22,66 @@ from attributes import attributes
 class GA(attributes):
 
     # Inhert all the ga attributes from the attributes class.
-    def __init__(self):             
+    def __init__(self,
-       super(GA, self).__init__()
+            chromosome_length = None,
+            population_size = None,
+            chromosome_impl = None,
+            gene_impl = None,
+            population = None,
+            target_fitness_type = None,
+            update_fitness = None,
+            parent_ratio = None,
+            selection_probability = None,
+            tournament_size_ratio = None,
+            current_generation = None,
+            current_fitness = None,
+            generation_goal = None,
+            fitness_goal = None,
+            chromosome_mutation_rate = None,
+            gene_mutation_rate = None,
+            initialization_impl = None,
+            fitness_function_impl = None,
+            make_population = None,
+            make_chromosome = None,
+            make_gene = None,
+            parent_selection_impl = None,
+            crossover_individual_impl = None,
+            crossover_population_impl = None,
+            survivor_selection_impl = None,
+            mutation_individual_impl = None,
+            mutation_population_impl = None,
+            termination_impl = None
+            ):             
+        super(GA, self).__init__(
+            chromosome_length,
+            population_size,
+            chromosome_impl,
+            gene_impl,
+            population,
+            target_fitness_type,
+            update_fitness,
+            parent_ratio,
+            selection_probability,
+            tournament_size_ratio,
+            current_generation,
+            current_fitness,
+            generation_goal,
+            fitness_goal,
+            chromosome_mutation_rate,
+            gene_mutation_rate,
+            initialization_impl,
+            fitness_function_impl,
+            make_population,
+            make_chromosome,
+            make_gene,
+            parent_selection_impl,
+            crossover_individual_impl,
+            crossover_population_impl,
+            survivor_selection_impl,
+            mutation_individual_impl,
+            mutation_population_impl,
+            termination_impl
+        )
 
 
     def evolve_generation(self, number_of_generations = 1, consider_termination = True):

src/attributes.py

@@ -21,50 +21,79 @@ from crossover import Crossover_Methods
 class attributes:
     """SAMPLE TEXT"""
 
-    def __init__(self):
+    def __init__(self,
+            chromosome_length,
+            population_size,
+            chromosome_impl,
+            gene_impl,
+            population,
+            target_fitness_type,
+            update_fitness,
+            parent_ratio,
+            selection_probability,
+            tournament_size_ratio,
+            current_generation,
+            current_fitness,
+            generation_goal,
+            fitness_goal,
+            chromosome_mutation_rate,
+            gene_mutation_rate,
+            initialization_impl,
+            fitness_function_impl,
+            make_population,
+            make_chromosome,
+            make_gene,
+            parent_selection_impl,
+            crossover_individual_impl,
+            crossover_population_impl,
+            survivor_selection_impl,
+            mutation_individual_impl,
+            mutation_population_impl,
+            termination_impl
+            ):
         """Initialize the GA."""
 
         # Initilization variables
-        self.chromosome_length   = 10
+        self.chromosome_length   = 10    if chromosome_length   is None else chromosome_length
-        self.population_size     = 10
+        self.population_size     = 10    if population_size     is None else population_size
-        self.chromosome_impl     = None
+        self.chromosome_impl     = chromosome_impl
-        self.gene_impl           = lambda: random.randint(1, 10)
+        self.gene_impl           = gene_impl
-        self.population          = None
+        self.population          = population
-        self.target_fitness_type = 'max'
+        self.target_fitness_type = 'max' if target_fitness_type is None else target_fitness_type
-        self.update_fitness      = True
+        self.update_fitness      = True  if update_fitness      is None else update_fitness
 
         # Selection variables
-        self.parent_ratio          = 0.1
+        self.parent_ratio = 0.10          if parent_ratio          is None else parent_ratio
-        self.selection_probability = 0.75
+        self.selection_probability = 0.75 if selection_probability is None else selection_probability
-        self.tournament_size_ratio = 0.1
+        self.tournament_size_ratio = 0.10 if tournament_size_ratio is None else tournament_size_ratio
 
         # Termination variables
-        self.current_generation = 0
+        self.current_generation = 0  if current_generation is None else current_generation
-        self.current_fitness    = 0
+        self.current_fitness    = 0  if current_fitness    is None else current_fitness
-        self.generation_goal    = 15
+        self.generation_goal    = 15 if generation_goal    is None else generation_goal
-        self.fitness_goal       = None
+        self.fitness_goal       = fitness_goal
 
         # Mutation variables
-        self.chromosome_mutation_rate = 0.10
+        self.chromosome_mutation_rate = 0.15 if chromosome_mutation_rate is None else chromosome_mutation_rate
-        self.gene_mutation_rate       = 0.01
+        self.gene_mutation_rate       = 0.03 if gene_mutation_rate       is None else gene_mutation_rate
 
         # Default EasyGA implimentation structure
-        self.initialization_impl   = Initialization_Methods.random_initialization
+        self.initialization_impl   = Initialization_Methods.random_initialization if initialization_impl is None else initialization_impl
-        self.fitness_function_impl = Fitness_Examples.is_it_5
+        self.fitness_function_impl = Fitness_Examples.is_it_5 if fitness_function_impl is None else fitness_function_impl
-        self.make_population       = create_population
+        self.make_population       = create_population if create_population is None else create_population
-        self.make_chromosome       = create_chromosome
+        self.make_chromosome       = create_chromosome if create_chromosome is None else create_chromosome
-        self.make_gene             = create_gene
+        self.make_gene             = create_gene       if create_gene       is None else create_gene
 
         # Methods for accomplishing Parent-Selection -> Crossover -> Survivor_Selection -> Mutation
-        self.parent_selection_impl     = Parent_Selection.Rank.tournament
+        self.parent_selection_impl     = Parent_Selection.Rank.tournament if parent_selection_impl is None else parent_selection_impl
-        self.crossover_individual_impl = Crossover_Methods.Individual.single_point
+        self.crossover_individual_impl = Crossover_Methods.Individual.single_point if crossover_individual_impl is None else crossover_individual_impl
-        self.crossover_population_impl = Crossover_Methods.Population.sequential_selection
+        self.crossover_population_impl = Crossover_Methods.Population.sequential_selection if crossover_population_impl is None else crossover_population_impl
-        self.survivor_selection_impl   = Survivor_Selection.fill_in_best
+        self.survivor_selection_impl   = Survivor_Selection.fill_in_best if survivor_selection_impl is None else survivor_selection_impl
-        self.mutation_individual_impl  = Mutation_Methods.Individual.single_gene
+        self.mutation_individual_impl  = Mutation_Methods.Individual.single_gene if mutation_individual_impl is None else mutation_individual_impl
-        self.mutation_population_impl  = Mutation_Methods.Population.random_selection
+        self.mutation_population_impl  = Mutation_Methods.Population.random_selection if mutation_population_impl is None else mutation_population_impl
 
         # The type of termination to impliment
-        self.termination_impl = Termination_Methods.fitness_and_generation_based
+        self.termination_impl = Termination_Methods.fitness_and_generation_based if termination_impl is None else termination_impl
 
 
     # Getter and setters for all varibles

src/run_testing.py

@@ -2,30 +2,31 @@ import random
 import EasyGA
 
 # Create the Genetic algorithm
-ga = EasyGA.GA()
-
 #   Reproduce 30% of the population.
 #   Mutate 20% of the population.
 #   Mutate 3% of the genes in each mutated chromosome.
-ga.parent_ratio  = 0.30
+#   
-ga.chromosome_mutation_rate = 0.20
-ga.gene_mutation_rate = 0.03
-
 #   Create 25 chromosomes each with 10 genes
-ga.population_size = 100
-ga.chromosome_length = 25
-
 #   Create random genes from 0 to 10
-ga.gene_impl = lambda: random.randint(0, 10)
+#   
-
 #   Minimize the sum of the genes
-ga.fitness_function_impl = lambda chromosome: sum(gene.get_value() for gene in chromosome.get_gene_list())
-ga.target_fitness_type = 'min'
-
 #   Terminate when a chromosome has all 0's
-ga.fitness_goal = 0
+#     or 150 generations pass
-ga.generation_goal = 150
+# 
+ga = EasyGA.GA(
+    parent_ratio = 0.30,
+    chromosome_mutation_rate = 0.20,
+    gene_mutation_rate = 0.03,
+    population_size = 100,
+    chromosome_length = 25,
+    gene_impl = lambda: random.randint(0, 10),
+    fitness_function_impl = lambda chromosome: sum(gene.get_value() for gene in chromosome.get_gene_list()),
+    target_fitness_type = 'min',
+    fitness_goal = 0,
+    generation_goal = 150
+)
 
+# Run 10 generations at a time
 while ga.active():
     ga.evolve_generation(10)
     ga.print_generation()