Updated Implementation Framework

Updated to cover changes made by Dan to Master regarding general design changes

Also added remove_two_worst survivor selection method

src/EasyGA.py

@@ -3,25 +3,29 @@ import random
 from initialization import Population as create_population
 from initialization import Chromosome as create_chromosome
 from initialization import Gene as create_gene
-# Import example classes
+# Structure Methods
 from fitness_function import Fitness_Examples
-from initialization import Initialization_Types
-from termination_point import Termination_Types
-from selection import Selection_Types
-from crossover import Crossover_Types
-from mutation import Mutation_Types
+from initialization import Initialization_Methods
+from termination_point import Termination_Methods
+# Population Methods
+from selection import Selection_Methods
+# Manipulation 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 = 150
+        self.population_size = 100
         self.chromosome_impl = None
         self.gene_impl = None
         self.population = None
         # Termination varibles
         self.current_generation = 0
+        self.generation_goal = 50
+
         self.current_fitness = 0
         self.generation_goal = 100
         self.fitness_goal = 3
@@ -32,19 +36,52 @@ class GA:
         self.update_fitness = True
 
         # Defualt EastGA implimentation structure
-        self.initialization_impl = Initialization_Types().random_initialization
+        self.initialization_impl = Initialization_Methods().random_initialization
         self.fitness_function_impl = Fitness_Examples().is_it_5
-        self.mutation_impl = Mutation_Types().per_gene_mutation
-        self.parent_selection_impl = Selection_Types().Parent_Selection().Tournament().with_replacement
-        self.survivor_selection_impl = Selection_Types().Survivor_Selection().repeated_crossover
-        self.crossover_impl = Crossover_Types().single_point_crossover
-        self.termination_impl = Termination_Types().generation_based
+        # Selects which chromosomes should be automaticly moved to the next population
+        self.survivor_selection_impl = Selection_Methods().Survivor_Selection().remove_two_worst
+        # Methods for accomplishing parent-selection -> Crossover -> Mutation
+        self.parent_selection_impl = Selection_Methods().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
+
+    def evolve_generation(self, number_of_generations = 1, consider_termination = True):
+        """Evolves the ga the specified number of generations."""
+        while(number_of_generations > 0 and (consider_termination == False or self.termination_impl(self))):
+            # If its the first generation then initialize the population
+            if self.current_generation == 0:
+                self.initialize_population()
+                self.set_all_fitness(self.population.chromosome_list)
+            
+            self.parent_selection_impl(self)
+            next_population = self.crossover_impl(self)
+            next_population = self.survivor_selection_impl(self, next_population)
+            next_population.set_all_chromosomes(self.mutation_impl(self, next_population.get_all_chromosomes()))
+
+            self.population = next_population
+            self.set_all_fitness(self.population.chromosome_list)
+
+            number_of_generations -= 1
+            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.
@@ -69,7 +106,7 @@ class GA:
             # If its the first generation then initialize the population
             if self.current_generation == 0:
                 self.initialize_population()
-                self.set_all_fitness(self.population.chromosomes)
+                self.set_all_fitness(self.population.chromosome_list)
             
             self.parent_selection_impl(self)
             next_population = self.crossover_impl(self)
@@ -77,7 +114,7 @@ class GA:
             next_population.set_all_chromosomes(self.mutation_impl(self, next_population.get_all_chromosomes()))
 
             self.population = next_population
-            self.set_all_fitness(self.population.chromosomes)
+            self.set_all_fitness(self.population.chromosome_list)
 
             number_of_generations -= 1
             self.current_generation += 1