Merge branch 'master' of https://github.com/danielwilczak101/EasyGA

src/fitness_function/fitness_examples.py

@@ -19,7 +19,7 @@ class Fitness_Examples:
         """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"""
         fitness = 0
-        for i in range(len(chromosome.gene_list)):
+        for i in range(chromosome.size()):
             if (chromosome.gene_list[i].value == i+1):
                 fitness += 1
                 

src/initialization/chromosome_structure/chromosome.py

@@ -9,6 +9,10 @@ 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:

src/parent_selection/parent_selection_methods.py

@@ -12,14 +12,14 @@ class Parent_Selection:
             The total number of parents selected is determined by parent_ratio, an attribute to the GA object.
             """
             
-            tournament_size = int(len(ga.population.get_all_chromosomes())*ga.tournament_size_ratio)
+            tournament_size = int(ga.population.size()*ga.tournament_size_ratio)
             if tournament_size < 5:
                 tournament_size = 5
             # 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):
+            while (len(ga.population.mating_pool) < ga.population.size()*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)])
                 
@@ -41,7 +41,7 @@ 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(len(ga.population.chromosome_list)))
+            total_fitness = sum(ga.population.chromosome_list[i].get_fitness() for i in range(ga.population.size()))
             rel_fitnesses = []
     
             for chromosome in ga.population.chromosome_list:
@@ -50,7 +50,7 @@ class Parent_Selection:
             
             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):
+            while (len(ga.population.mating_pool) < ga.population.size()*ga.parent_ratio):
                 rand_number = random.random()
     
                 # Loop through the list of probabilities

src/termination_point/termination_methods.py

@@ -3,19 +3,15 @@ class Termination_Methods:
 
     def fitness_based(ga):
         """Fitness based approach to terminate when the goal fitness has been reached"""
-            
-        status = True
+        
         if ga.population == None:
-            return status
-        for i in range(len(ga.population.get_all_chromosomes())):
+            return True
+        for i in range(ga.population.size()):
             if(ga.population.get_all_chromosomes()[i].fitness >= ga.fitness_goal):
-                status = False
-                break
-        return status
+                return False
+        return True
 
     def generation_based(ga):
         """Generation based approach to terminate when the goal generation has been reached"""
-        status = True
-        if(ga.current_generation > ga.generation_goal):
-            status = False
-        return status
+        
+        return ga.current_generation < ga.generation_goal