Implemented basic functionality for using different target fitness types

src/EasyGA.py

@@ -80,4 +80,26 @@ class GA(attributes):
 
         return sorted(chromosome_set,                                    # list to be sorted
                      key = lambda chromosome: chromosome.get_fitness(),  # by fitness
-                     reverse = True)                                     # from highest to lowest fitness
+                     reverse = (self.target_fitness_type == 'max'))      # from highest to lowest fitness
+
+
+    def get_chromosome_fitness(self, index):
+        """Returns the fitness value of the chromosome
+        at the specified index after conversion based
+        on the target fitness type.
+        """
+        return self.convert_fitness(
+                   self.population.get_chromosome(index).get_fitness()
+               )
+
+
+    def convert_fitness(self, fitness_value):
+        """Returns the fitness value if the type of problem
+        is a maximization problem. Otherwise the fitness is
+        inverted using max - value + min.
+        """
+
+        if self.target_fitness_type == 'max': return fitness_value
+        max_fitness = self.population.get_chromosome(-1).get_fitness()
+        min_fitness = self.population.get_chromosome(0).get_fitness()
+        return max_fitness - fitness_value + min_fitness

src/attributes.py

@@ -28,7 +28,7 @@ class attributes:
         self.chromosome_impl     = None
         self.gene_impl           = lambda: random.randint(1, 10)
         self.population          = None
-        self.target_fitness_type = 'maximum'
+        self.target_fitness_type = 'max'
         self.update_fitness      = True
 
         # Selection variables

src/parent_selection/parent_selection_methods.py

@@ -66,20 +66,20 @@ class Parent_Selection:
                 return
 
             # Error if not all chromosomes has positive fitness
-            if (ga.population.get_chromosome(0).get_fitness() == 0 or ga.population.get_chromosome(-1).get_fitness() < 0):
+            if (ga.get_chromosome_fitness(0) == 0 or ga.get_chromosome_fitness(-1) < 0):
                 print("Error using roulette selection, all fitnesses must be positive.")
                 print("Consider using stockastic roulette selection or tournament selection.")
                 return
 
             # The sum of all the fitnessess in a population
-            fitness_sum = sum(chromosome.get_fitness() for chromosome in ga.population.get_all_chromosomes())
+            fitness_sum = sum(ga.get_chromosome_fitness(index) for index in range(ga.population.size()))
 
             # A list of ranges that represent the probability of a chromosome getting chosen
             probability = [ga.selection_probability]
 
             # The chance of being selected increases incrementally
-            for chromosome in ga.population.chromosome_list:
+            for index in range(ga.population.size()):
-                probability.append(probability[-1]+chromosome.fitness/fitness_sum)
+                probability.append(probability[-1]+ga.get_chromosome_fitness(index)/fitness_sum)
 
             probability = probability[1:]
 
@@ -111,7 +111,7 @@ class Parent_Selection:
                 print("Selection probability must be between 0 and 1 to select parents.")
                 return
 
-            max_fitness = ga.population.get_chromosome(0).get_fitness()
+            max_fitness = ga.get_chromosome_fitness(0)
 
             # Error if the highest fitness is not positive
             if max_fitness <= 0:
@@ -126,5 +126,5 @@ class Parent_Selection:
                 index = random.randint(0, ga.population.size()-1)
 
                 # Probability of becoming a parent is fitness/max_fitness
-                if random.uniform(ga.selection_probability, 1) < ga.population.get_chromosome(index).get_fitness()/max_fitness:
+                if random.uniform(ga.selection_probability, 1) < ga.get_chromosome_fitness(index)/max_fitness:
                     ga.population.set_parent(index)

src/run_testing.py

@@ -3,10 +3,22 @@ import EasyGA
 
 # Create the Genetic algorithm
 ga = EasyGA.GA()
+ga.target_fitness_type = 'min'
+ga.chromosome_length = 10
+ga.population_size = 25
+ga.generation_goal = 50
+ga.gene_impl = lambda: random.randint(0, 10)
 
-ga.chromosome_length = 100
+def fitness_function(chromosome):
+    return sum(
+               gene.get_value()
+           for gene in chromosome.get_gene_list())
+
+ga.fitness_function_impl = fitness_function
 
 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/survivor_selection/survivor_selection_methods.py

@@ -27,16 +27,10 @@ class Survivor_Selection:
     def fill_in_parents_then_random(ga):
         """Fills in the next population with all parents followed by random chromosomes from the last population"""
 
-        ga.population.append_children([              # add in chromosomes
-            random.choice(                           #     randomly
-                ga.population.get_chromosome_list()  #         from the population
-            )                                        # until the next population is full
-        for n in range(ga.population.size()-ga.population.total_children())])
-
-
         ga.population.append_children(               # add in chromosomes
             ga.population.get_mating_pool()          #     from the mating pool
-        )                                            # 
+        )                                            #
+
         ga.population.append_children([              # add in chromosomes
             random.choice(                           #     randomly
                 ga.population.get_chromosome_list()  #         from the population