Simplifying the example

Removing some comments and variables

Author: ahmedfgad

example.py

@@ -12,62 +12,38 @@
 desired_output = 44 # Function output.
 
 def fitness_func(solution, solution_idx):
-    # Calculating the fitness value of each solution in the current population.
-    # The fitness function calulates the sum of products between each input and its corresponding weight.
     output = numpy.sum(solution*function_inputs)
-    # The value 0.000001 is used to avoid the Inf value when the denominator numpy.abs(output - desired_output) is 0.0.
     fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001)
     return fitness
 
-fitness_function = fitness_func
-
 num_generations = 100 # Number of generations.
 num_parents_mating = 10 # Number of solutions to be selected as parents in the mating pool.
 
-# To prepare the initial population, there are 2 ways:
-# 1) Prepare it yourself and pass it to the initial_population parameter. This way is useful when the user wants to start the genetic algorithm with a custom initial population.
-# 2) Assign valid integer values to the sol_per_pop and num_genes parameters. If the initial_population parameter exists, then the sol_per_pop and num_genes parameters are useless.
 sol_per_pop = 20 # Number of solutions in the population.
 num_genes = len(function_inputs)
 
-parent_selection_type = "sss" # Type of parent selection.
-keep_parents = -1 # Number of parents to keep in the next population. -1 means keep all parents and 0 means keep nothing.
-
-crossover_type = "single_point" # Type of the crossover operator.
-
-# Parameters of the mutation operation.
-mutation_type = "random" # Type of the mutation operator.
-mutation_percent_genes = 10 # Percentage of genes to mutate. This parameter has no action if the parameter mutation_num_genes exists or when mutation_type is None.
-
 last_fitness = 0
-def callback_generation(ga_instance):
+def on_generation(ga_instance):
     global last_fitness
     print("Generation = {generation}".format(generation=ga_instance.generations_completed))
     print("Fitness    = {fitness}".format(fitness=ga_instance.best_solution(pop_fitness=ga_instance.last_generation_fitness)[1]))
     print("Change     = {change}".format(change=ga_instance.best_solution(pop_fitness=ga_instance.last_generation_fitness)[1] - last_fitness))
     last_fitness = ga_instance.best_solution(pop_fitness=ga_instance.last_generation_fitness)[1]
 
-# Creating an instance of the GA class inside the ga module. Some parameters are initialized within the constructor.
 ga_instance = pygad.GA(num_generations=num_generations,
                        num_parents_mating=num_parents_mating,
-                       fitness_func=fitness_function,
                        sol_per_pop=sol_per_pop,
                        num_genes=num_genes,
-                       parent_selection_type=parent_selection_type,
-                       keep_parents=keep_parents,
-                       crossover_type=crossover_type,
-                       mutation_type=mutation_type,
-                       mutation_percent_genes=mutation_percent_genes,
-                       on_generation=callback_generation)
+                       fitness_func=fitness_func,
+                       on_generation=on_generation)
 
 # Running the GA to optimize the parameters of the function.
 ga_instance.run()
 
-# After the generations complete, some plots are showed that summarize the how the outputs/fitenss values evolve over generations.
 ga_instance.plot_result()
 
 # Returning the details of the best solution.
-solution, solution_fitness, solution_idx = ga_instance.best_solution()
+solution, solution_fitness, solution_idx = ga_instance.best_solution(ga_instance.last_generation_fitness)
 print("Parameters of the best solution : {solution}".format(solution=solution))
 print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness))
 print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx))