# -*- coding: utf-8 -*- import AG import Graphic # Parámetros del algoritmo. Npob = 200 chromosomeWidth = 12 generations = 50 mutationProbability = 30 selectionPercentage = 30 # Algoritmo principal pop = AG.ini_pob(Npob, chromosomeWidth) bestFitness = [] average = [] for i in range(generations): print 'Iteracion: {}'.format(i) fitPop = AG.fitness(pop) maxFitness = max(fitPop) averageFit = sum(fitPop)/len(pop) bestFitness.append(maxFitness) average.append(averageFit) print 'Mejor fitness: {}\nMedia: {}'.format(maxFitness, averageFit) selPop = AG.truncateSelection(fitPop, pop, selectionPercentage) pop = AG.uniformCrossover(Npob, selPop, mutationProbability) Graphic.fitness_average(bestFitness, average)