def genetic_alg(cls, iteration_num, parent_num, fitness,
number_of_solutions, num_genes, crossover_probability,
mutation_probability, after_mutation_func):
# initial_population is built by sol_per_pop and num_genes
# num_genes = Number of genes in the solution / chromosome
ga_instance = pygad.GA(num_generations=iteration_num,
num_parents_mating=parent_num,
fitness_func=fitness,
sol_per_pop=number_of_solutions,
num_genes=num_genes,
gene_type=float,
init_range_low=0.0,
init_range_high=1.0,
parent_selection_type="rws",
keep_parents=0,
crossover_type="single_point",
crossover_probability=crossover_probability,
mutation_type="random",
mutation_probability=mutation_probability,
mutation_by_replacement=False,
random_mutation_min_val=0.0,
random_mutation_max_val=1.0,
on_mutation=after_mutation_func)
ga_instance.run()
solution, solution_fitness, solution_idx = ga_instance.best_solution()
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))
filename = 'genetic'
ga_instance.save(filename=filename)
loaded_ga_instance = pygad.load(filename=filename)
return loaded_ga_instance.best_solution()
def fit(self):
parameters = self.get_genetic_parameters()
if os.path.exists(self.path['GA'] + '.pkl'):
self.ga = pygad.load(self.path['GA'])
self.ga.fitness_func = fitness_function_factory(self)
else:
self.ga = pygad.GA(**parameters)
self.ga.path = self.path['GA']
if len(self.ga.solutions) < (self.ga.num_generations +
1) * self.ga.sol_per_pop:
self.ga.run()
return
num_parents_mating=num_parents_mating,
sol_per_pop=sol_per_pop,
num_genes=num_genes,
fitness_func=fitness_func,
on_generation=on_generation)
# Running the GA to optimize the parameters of the function.
ga_instance.run()
ga_instance.plot_fitness()
# Returning the details of the 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))
prediction = numpy.sum(numpy.array(function_inputs)*solution)
print("Predicted output based on the best solution : {prediction}".format(prediction=prediction))
if ga_instance.best_solution_generation != -1:
print("Best fitness value reached after {best_solution_generation} generations.".format(best_solution_generation=ga_instance.best_solution_generation))
# Saving the GA instance.
filename = 'genetic' # The filename to which the instance is saved. The name is without extension.
ga_instance.save(filename=filename)
# Loading the saved GA instance.
loaded_ga_instance = pygad.load(filename=filename)
loaded_ga_instance.plot_fitness()
def Run(testset_size, test_weight):
desired_output = 100
def fitness_func(solution, solution_idx):
output = fuzzification.Run(solution, testset_size, test_weight)
fitness = 1.0 / numpy.abs(output - desired_output)
return fitness
fitness_function = fitness_func
num_generations = 100
num_parents_mating = 7
sol_per_pop = 50
num_genes = 8
init_range_low = -2
init_range_high = 40
parent_selection_type = "sss"
keep_parents = 7
crossover_type = "single_point"
mutation_type = "random"
mutation_percent_genes = 10
def callback_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))
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,
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,
callback_generation=callback_generation)
ga_instance.run()
ga_instance.plot_result()
solution, solution_fitness, solution_idx = ga_instance.best_solution()
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))
#prediction = numpy.sum(numpy.array(function_inputs)*solution)
#print("Predicted output based on the best solution : {prediction}".format(prediction=prediction))
if ga_instance.best_solution_generation != -1:
print(
"Best fitness value reached after {best_solution_generation} generations."
.format(
best_solution_generation=ga_instance.best_solution_generation))
filename = 'genetic'
ga_instance.save(filename=filename)
loaded_ga_instance = pygad.load(filename=filename)
loaded_ga_instance.plot_result()
