Updated selection implementation, added with/without replacement variation

src/EasyGA.py

@@ -23,10 +23,10 @@ class GA:
         # Termination varibles
         self.current_generation = 0
         self.current_fitness = 0
-        self.generation_goal = 35
+        self.generation_goal = 50
         self.fitness_goal = 3
         # Mutation variables
-        self.mutation_rate = 0.075
+        self.mutation_rate = 0.05
 
         # Rerun already computed fitness
         self.update_fitness = True
@@ -35,7 +35,7 @@ class GA:
         self.initialization_impl = Initialization_Types().random_initialization
         self.fitness_function_impl = Fitness_Examples().is_it_5
         self.mutation_impl = Mutation_Types().random_mutation
-        self.selection_impl = Selection_Types().tournament_selection
+        self.selection_impl = Selection_Types().Tournament().with_replacement
         self.crossover_impl = Crossover_Types().single_point_crossover
         self.termination_impl = Termination_Types().generation_based
 

src/crossover/crossover_types.py

@@ -25,9 +25,10 @@ class Crossover_Types:
                 new_gene_set = []
                 parent_one = crossover_pool[i].get_genes()
                 parent_two = crossover_pool[i+1].get_genes()
-                halfway_point = int(ga.chromosome_length/2)
+                #halfway_point = int(ga.chromosome_length/2)
-                new_gene_set.extend(parent_one[0:halfway_point])
+                split_point = random.randint(0,ga.chromosome_length)    
-                new_gene_set.extend(parent_two[halfway_point:])
+                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)
 

src/run_testing.py

@@ -5,9 +5,9 @@ import random
 # Create the Genetic algorithm
 ga = EasyGA.GA()
 
-ga.gene_impl = [random.randrange,1,10]
+ga.gene_impl = [random.randrange,1,25]
 
-# Run Everyhting
+# Run Everything
 ga.evolve()
 
 # Print the current population

src/selection/selection_types.py

@@ -9,22 +9,69 @@ class Selection_Types:
     def __init__(self):
         pass
 
-    def tournament_selection(self, ga):
+    class Tournament:
-        """This example currently uses a 'with replacement' approach (chromosomes are placed back into the pool after participating)"""
+        def with_replacement(self, ga):
             tournament_size = int(len(ga.population.get_all_chromosomes())/10) #currently hard-coded for purposes of the example.
+            if tournament_size < 3:
+                tournament_size = int(len(ga.population.get_all_chromosomes())/3)
 
             #selection_probability is the likelihood that a chromosome will be selected.
             #best chromosome in a tournament is given a selection probablity of selection_probability
             #2nd best is given probability of selection_probability*(1-selection_probability)
+            #3rd best is given probability of selection_probability*(1-selection_probability)**2
             selection_probability = 0.95
             total_selected = 0 #Total Chromosomes selected
 
             while (total_selected <= ga.population_size*2):
                 #create & gather tournament group
                 tournament_group = []
+
                 for i in range(tournament_size):
                     tournament_group.append(random.choice(ga.population.get_all_chromosomes()))
+                total_selected = self.selection(tournament_group, tournament_size, total_selected, selection_probability)[0]
 
+            new_population = self.create_new_population(ga)
+            return new_population
+
+        def without_replacement(self, ga):
+            tournament_size = int(len(ga.population.get_all_chromosomes())/10) #currently hard-coded for purposes of the example.
+            if tournament_size < 3:
+                tournament_size = int(len(ga.population.get_all_chromosomes())/3)
+
+            #selection_probability is the likelihood that a chromosome will be selected.
+            #best chromosome in a tournament is given a selection probablity of selection_probability
+            #2nd best is given probability of selection_probability*(1-selection_probability)
+            #3rd best is given probability of selection_probability*(1-selection_probability)**2
+            selection_probability = 0.95
+            total_selected = 0 #Total Chromosomes selected
+            available_chromosome_indices = []
+            for i in range(len(ga.population.get_all_chromosomes())):
+                available_chromosome_indices.append(i)
+
+            continue_selecting = True
+
+            while (continue_selecting):
+                #create & gather tournament group
+                tournament_group = []
+
+                for i in range(tournament_size):
+                    selected_chromosome_index = random.choice(available_chromosome_indices)
+                    tournament_group.append(ga.population.get_all_chromosomes()[selected_chromosome_index])
+
+                winning_chromosome_index = self.selection(tournament_group, tournament_size, total_selected, selection_probability)[1]
+                for i in range(len(available_chromosome_indices)):
+                    if tournament_group[winning_chromosome_index].selected:
+                        del available_chromosome_indices[i]
+                        break
+                #print(winning_chromosome_index)
+                #print(available_chromosome_indices)
+                if len(available_chromosome_indices) < 1:
+                    continue_selecting = False
+
+            new_population = self.create_new_population(ga)
+            return new_population
+
+        def selection(self, tournament_group, tournament_size, total_selected, selection_probability):
             #Sort the tournament contenders based on their fitness
             #currently hard-coded to only consider higher fitness = better; can be changed once this impl is agreed on
             #also currently uses bubble sort because its easy
@@ -43,6 +90,7 @@ class Selection_Types:
             tournament_group = tournament_group_temp
 
             #After sorting by fitness, randomly select a chromosome based on selection_probability
+            selected_chromosome_tournament_index = 0
             for i in range(tournament_size):
                 random_num = random.uniform(0,1)
 
@@ -51,14 +99,18 @@ class Selection_Types:
                     if random_num <= selection_probability:
                         tournament_group[i].selected = True
                         total_selected += 1
+                        selected_chromosome_tournament_index = i
                         break
                 else:
                     if random_num <= selection_probability*((1-selection_probability)**(i-1)):
                         tournament_group[i].selected = True
                         total_selected += 1
+                        selected_chromosome_tournament_index = i
                         break
 
+            return total_selected,selected_chromosome_tournament_index
 
+        def create_new_population(self, ga):
             new_population = ga.crossover_impl(ga)
 
             #If the crossover doesn't create enough chromosomes (ugly right now pls no judgerino, can be changed)
@@ -91,8 +143,6 @@ class Selection_Types:
 
             return new_population
 
-
-
     def roulette_selection(self, 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