Updated fitness based termination and fixed some EasyGA stuff

src/EasyGA.py

@@ -1,4 +1,20 @@
-import random
+# Import all the data structure prebuilt modules
+from structure import Population as create_population
+from structure import Chromosome as create_chromosome
+from structure import Gene as create_gene
+
+# Structure Methods
+from fitness_function  import Fitness_Examples
+from initialization    import Initialization_Methods
+from termination_point import Termination_Methods
+
+# Parent/Survivor Selection Methods
+from parent_selection   import Parent_Selection
+from survivor_selection import Survivor_Selection
+
+# Genetic Operator Methods
+from mutation  import Mutation_Methods
+from crossover import Crossover_Methods
 
 from attributes import attributes
 
@@ -13,9 +29,9 @@ class GA(attributes):
     def evolve_generation(self, number_of_generations = 1, consider_termination = True):
         """Evolves the ga the specified number of generations."""
 
-        while(number_of_generations > 0               # Evolve the specified number of generations
+        while(number_of_generations > 0      # Evolve the specified number of generations
-              and (not consider_termination           #     and if consider_termination flag is set
+              and (not consider_termination  #     and if consider_termination flag is set
-                   or self.termination_impl(self))):  #         then also check if termination conditions reached
+                   or self.active())):       #         then also check if termination conditions reached
 
             # If its the first generation then initialize the population
             if self.current_generation == 0:
@@ -25,13 +41,13 @@ class GA(attributes):
 
             # Otherwise evolve the population
             else:
-                self.set_all_fitness()
-                self.population.sort_by_best_fitness(self)
                 self.parent_selection_impl(self)
                 self.crossover_population_impl(self)
                 self.survivor_selection_impl(self)
                 self.mutation_population_impl(self)
                 self.population.update()
+                self.set_all_fitness()
+                self.population.sort_by_best_fitness(self)
 
             number_of_generations -= 1
             self.current_generation += 1

src/run_testing.py

@@ -3,22 +3,23 @@ import EasyGA
 
 # Create the Genetic algorithm
 ga = EasyGA.GA()
-ga.target_fitness_type = 'min'
+
-ga.chromosome_length = 10
+# Create 25 chromosomes each with 10 genes
 ga.population_size = 25
-ga.generation_goal = 50
+ga.chromosome_length = 10
+
+# Create random genes from 0 to 10
 ga.gene_impl = lambda: random.randint(0, 10)
 
-def fitness_function(chromosome):
+# Minimize the sum of the genes
-    return sum(
+ga.fitness_function_impl = lambda chromosome: sum(gene.get_value() for gene in chromosome.get_gene_list())
-               gene.get_value()
+ga.target_fitness_type = 'min'
-           for gene in chromosome.get_gene_list())
 
-ga.fitness_function_impl = fitness_function
+# Terminate when a chromosome has all 0's
+ga.fitness_goal = 0
+ga.termination_impl = EasyGA.Termination_Methods.fitness_based
 
 ga.evolve()
-ga.set_all_fitness()
-ga.population.sort_by_best_fitness(ga)
 
 print(f"Current Generation: {ga.current_generation}")
 ga.population.print_all()

src/termination_point/termination_methods.py

@@ -4,22 +4,23 @@ class Termination_Methods:
     def fitness_based(ga):
         """Fitness based approach to terminate when the goal fitness has been reached"""
 
-        # Need to start the algorithm if the population is None
+        # Need to start the algorithm if the population is None.
         if ga.population == None:
             return True
 
-        # Check all chromosomes
+        # If minimum fitness goal reached, stop ga.
-        for chromosome in ga.population.get_all_chromosomes():
+        if ga.target_fitness_type == 'min' and ga.get_chromosome_fitness(0) >= ga.convert_fitness(ga.fitness_goal):
+            return False
 
-            # Stop if a chromosome has reached the fitness_goal
+        # If maximum fitness goal reached, stop ga.
-            if(chromosome.fitness >= ga.fitness_goal):
+        if ga.target_fitness_type == 'max' and ga.get_chromosome_fitness(0) >= ga.convert_fitness(ga.fitness_goal):
-                return False
+            return False
-
+        
-        # Continue if no chromosomes have reached the fitness goal
+        # Otherwise continue ga.
         return True
 
 
     def generation_based(ga):
-        """Generation based approach to terminate when the goal generation has been reached"""
+        """Generation based approach to terminate when the goal generation has been reached."""
 
         return ga.current_generation < ga.generation_goal