Neatified ga.print stuff and altered run_testing

src/EasyGA.py

@@ -119,3 +119,19 @@ class GA(attributes):
         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
+
+
+    def print_generation(self):
+        """Prints the current generation"""
+        print(f"Current Generation: {self.current_generation}")
+
+
+    def print_population(self):
+        """Prints the entire population"""
+        self.population.print_all()
+
+
+    def print_best(self):
+        """Prints the best chromosome and its fitness"""
+        print(f"Best Chromosome \t: {self.population.get_chromosome(0)}")
+        print(f"Best Fitness    \t: {self.population.get_chromosome(0).get_fitness()}")

src/run_testing.py

@@ -4,8 +4,12 @@ import EasyGA
 # Create the Genetic algorithm
 ga = EasyGA.GA()
 
+# Mutate and reproduce frequently
+ga.parent_ratio  = 0.25
+ga.mutation_rate = 0.25
+
 # Create 25 chromosomes each with 10 genes
-ga.population_size = 25
+ga.population_size = 50
 ga.chromosome_length = 10
 
 # Create random genes from 0 to 10
@@ -19,7 +23,9 @@ ga.target_fitness_type = 'min'
 ga.fitness_goal = 0
 ga.generation_goal = None
 
-ga.evolve()
+while ga.active():
-
+    ga.evolve_generation(10)
-print(f"Current Generation: {ga.current_generation}")
+    ga.print_generation()
-ga.population.print_all()
+    ga.print_best()
+    #ga.print_population()
+    print('-'*75)

src/termination_point/termination_methods.py

@@ -16,7 +16,7 @@ class Termination_Methods:
                 return False
 
             # If maximum fitness goal reached, stop ga.
-            if ga.target_fitness_type == 'max' and ga.get_chromosome_fitness(0) >= ga.convert_fitness(ga.fitness_goal):
+            elif ga.target_fitness_type == 'max' and ga.get_chromosome_fitness(0) >= ga.convert_fitness(ga.fitness_goal):
                 return False
 
         # If generation goal is set, check it.