Updated GA attribute structure, separated selection file structure

Updated GA attribute structure, separated selection file structure

src/parent_selection/__init__.py

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

src/parent_selection/methods.py

@@ -1,37 +0,0 @@
-class Parent_methods:
-    """Selection defintion here"""
-
-    def tournament_selection(ga,matchs):
-         """Tournament selection involves running several "tournaments" among a
-        few individuals (or "chromosomes")chosen at random from the population.
-        The winner of each tournament (the one with the best fitness) is selected
-        for crossover.
-        Ex
-        Chromsome 1----1 wins ------
-        Chromsome 2----            - --1 wins----
-                                   -            -
-        Chromsome 3----3 wins ------            -- 5 Wins --->Chromosome 5 becomes Parent
-        Chromsome 4----                         -
-                                                -
-        Chromsome 5----5 wins ---------5 wins----
-        Chromsome 6----
-       ^--Matchs--^
-        """
-
-    def small_tournament(ga):
-        """ Small tournament is only one round of tournament. Beat the other
-        randomly selected chromosome and your are selected as a parent.
-        Chromosome 1----
-                        -- 1 wins -> Becomes selected for crossover.
-        Chromosome 2----
-        """
-        pass
-
-    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
-        that it will be selected. Using the example of a casino roulette wheel.
-        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"""
-        pass

src/parent_selection/parent_selection.py

@@ -0,0 +1,59 @@
+import random
+from initialization.chromosome_structure.chromosome import Chromosome as create_chromosome
+from initialization.gene_structure.gene import Gene as create_gene
+from initialization.population_structure.population import Population
+from initialization.chromosome_structure.chromosome import Chromosome
+
+class Parent_Selection:
+    class Tournament:
+        def with_replacement(ga):
+            tournament_size = int(len(ga.population.get_all_chromosomes())*ga.parent_ratio/10)
+            if tournament_size < 3:
+                tournament_size = int(len(ga.population.get_all_chromosomes())*ga.parent_ratio/3)
+            
+            # Probability used for determining if a chromosome should enter the mating pool.
+            selection_probability = ga.selection_probability
+            
+            # Repeat tournaments until the mating pool is large enough.
+            while (len(ga.population.mating_pool) < len(ga.population.get_all_chromosomes())*ga.parent_ratio):
+                
+                # Generate a random tournament group and sort by fitness.
+                tournament_group = ga.sort_by_best_fitness([random.choice(ga.population.get_all_chromosomes()) for n in range(tournament_size)])
+                
+                # For each chromosome, add it to the mating pool based on its rank in the tournament.
+                for index in range(tournament_size):
+                    # Probability required is selection_probability * (1-selection_probability) ^ (tournament_size-index+1)
+                    # e.g. top ranked fitness has probability: selection_probability
+                    #   second ranked fitness has probability: selection_probability * (1-selection_probability)
+                    #   third  ranked fitness has probability: selection_probability * (1-selection_probability)^2
+                    # etc.
+                    if random.uniform(0, 1) < selection_probability * pow(1-selection_probability, index+1):
+                        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
+            that it will be selected. Using the example of a casino roulette wheel.
+            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(len(ga.population.chromosome_list)))
+            rel_fitnesses = []
+    
+            for chromosome in ga.population.chromosome_list:
+                if (total_fitness != 0):
+                    rel_fitnesses.append(float(chromosome.fitness)/total_fitness)
+            
+            probability = [sum(rel_fitnesses[:i+1]) for i in range(len(rel_fitnesses))]
+    
+            while (len(ga.population.mating_pool) < len(ga.population.get_all_chromosomes())*ga.parent_ratio):
+                rand_number = random.random()
+    
+                # Loop through the list of probabilities
+                for i in range(len(probability)):
+                    # If the probability is greater than the random_number, then select that chromosome
+                    if (probability[i] >= rand_number):
+                        ga.population.mating_pool.append(ga.population.chromosome_list[i])
+                        # print (f'Selected chromosome : {i}')
+                        break