Add files via upload

Author: ahmedfgad

docs/source/README_pygad_ReadTheDocs.rst

@@ -1665,66 +1665,38 @@ which optimizes a linear model. Its complete code is listed below.
    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)
-       fitness = 1.0 / numpy.abs(output - desired_output)
+       fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001)
        return fitness
 
-   fitness_function = fitness_func
-
-   num_generations = 50 # Number of generations.
-   num_parents_mating = 4 # Number of solutions to be selected as parents in the mating pool.
+   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 = 8 # Number of solutions in the population.
+   sol_per_pop = 20 # Number of solutions in the population.
    num_genes = len(function_inputs)
 
-   init_range_low = -2
-   init_range_high = 5
-
-   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.
-
    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()[1]))
-       print("Change     = {change}".format(change=ga_instance.best_solution()[1] - last_fitness))
-       last_fitness = ga_instance.best_solution()[1]
+       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_parents_mating=num_parents_mating,
+                          sol_per_pop=sol_per_pop,
                           num_genes=num_genes,
-                          init_range_low=init_range_low,
-                          init_range_high=init_range_high,
-                          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))