algoritimo-genetico.py

from gaft import GAEngine
from gaft.components import BinaryIndividual, Population
from gaft.operators import RouletteWheelSelection, UniformCrossover, FlipBitMutation
from gaft.plugin_interfaces.analysis import OnTheFlyAnalysis
from gaft.analysis.fitness_store import FitnessStore
from math import cos, sin
import matplotlib.pyplot as plt
import numpy as np

# Parametros para geração dos individuos
individuo_template = BinaryIndividual(ranges=[(0, 10)], eps=0.001)

# Parametros para inicializar a população
population = Population(indv_template=individuo_template, size=50)

# Parametros para inicializar a população
population.init()

# Parametros para execução dos operadores
selection = RouletteWheelSelection()
crossover = UniformCrossover(pc=0.5, pe=0.5)
mutation = FlipBitMutation(pm=0.1)

engine = GAEngine(population=population,
                  selection=selection, crossover=crossover, mutation=mutation, analysis=[FitnessStore])

# função de aptidão
@engine.fitness_register
def fitness(indv):
    x, = indv.solution
    return x + 10*sin(5*x) + 7*cos(4*x)


@engine.analysis_register
class ConsoleOutPut(OnTheFlyAnalysis):
    interval = 1

    def register_step(self, g, population, engine):
        best_indv = population.best_indv(engine.fitness)
        msg = 'Geração: {}, best fitness {:.3f}'.format(g, engine.fmax)
        engine.logger.info(msg)


def finalize(population, engine):
    best_indv = population.best_indv(engine.fitness)
    x = best_indv.solution
    y = engine.ori_fmax
    msg = 'Solução ótima: ({},{})'.format(x, y)
    engine.logger.info(msg)


def remove_characters(to_remove, line):
    line.replace(to_remove, "")
    return line


def plotAnalise():
    geracao = []  # lista de gerações
    fitness = []  # lista de aptidão

    best_fit_file = open("best_fit-1.py", "r")

    for line in best_fit_file:
        new_line = line.replace("[", "")
        new_line = new_line.replace("(", "")
        new_line = new_line.replace(")", "")
        new_line = new_line.replace(",", "")
        new_line = new_line.replace("]", "")
        lista = new_line.split()
        geracao.append(int(lista[0]))
        fitness.append(float(lista[2]))
    # print(geracao, fitness)
    figure = plt.figure()
    ax = figure.add_subplot(111)
    ax.plot(geracao, fitness)
    ax.set_xlabel('Gerações')
    ax.set_ylabel('Fitness')
    plt.title("Grafico daora")
    plt.show()


plotAnalise()
# if '__main__' == __name__:
#     engine.run(ng=100)