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November 30, 2017 20:47
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| # -*- coding: utf-8 -*- | |
| """ | |
| Introducción a los Algoritmos Geneticos | |
| Autor: Guillermo Izquierdo | |
| Este código es para fines educativos exclusivamente. | |
| """ | |
| geneSet = 'abcdefghijklmnñopqrstuvwxyzABCDEFGHIJKLMNÑOPQRSTUVWXYZ ' | |
| target = 'Hola Mundo' | |
| import datetime | |
| import random | |
| random.seed(2) | |
| startTime = datetime.datetime.now() | |
| #Funcion para generar de manera aleatoria una muestra de genes | |
| def generate_parent(length): | |
| genes = [] #Lista donde se almacenan las secuencia aleatoria | |
| while len(genes) < length: | |
| sampleSize = min(length - len(genes), len(geneSet)) | |
| genes.extend(random.sample(geneSet,sampleSize)) #Obtención de la muestra aleatoria | |
| return ''.join(genes) #Regresamos una cadena | |
| #Funcion de optimización, si el nuestra muestra aleatoria tiene un caracter igual a nuestro target | |
| def get_fitness(guess): | |
| return sum(1 for expected, actual in zip(target,guess) if expected == actual) | |
| #Funcion para mutar a nuestra cadena original o padre | |
| def mutate(parent): | |
| index = random.randrange(0,len(parent)) | |
| childGenes = list(parent) | |
| newGene, alternate = random.sample(geneSet,2) | |
| childGenes[index] = alternate if newGene == childGenes[index] else newGene | |
| return ''.join(childGenes) | |
| #Funcion para imprimir en pantalla los resultados | |
| def display(guess): | |
| timeDiff = datetime.datetime.now() - startTime | |
| fitness = get_fitness(guess) | |
| print('{}\t{}\t{}'.format(guess,fitness,timeDiff)) | |
| #Inicializamos nuestros parametros | |
| bestParent = generate_parent(len(target)) | |
| bestFitness = get_fitness(bestParent) | |
| display(bestParent) | |
| #Creamos un ciclo para iterar nuestras funciones | |
| #Hasta obtener nuestro target | |
| while True: | |
| child = mutate(bestParent) | |
| childFitness = get_fitness(child) | |
| if bestFitness >= childFitness: | |
| continue | |
| display(child) | |
| if childFitness >= len(bestParent): | |
| break | |
| bestFitness = childFitness | |
| bestParent = child |
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