algoritmo_genetico.py

import random

modelo = [2,2,3,3,3,4,4,4,8,8]
largo = 10
num = 10
pressure = 3 #cuantos individuos se seleccionan para reproducirce
mutation_chance = 0.2 #La probabilida de que un individuo sea mutado
print("\n\nModelo: %s\n"%(modelo))

def individual(min, max):
	return [random.randint(min, max) for i in range(largo)]

def crearPoblacion():
	return [individual(1,9) for i in range(num)]

def calcularFitness(individual):
	fitness = 0
	for i in range(len(individual)):
		if individual[i] == modelo[i]:
			fitness += 1
	return fitness

def selection_and_reproduction(population):
	puntuados = [(calcularFitness(i), i) for i in population]
	puntuados = [i[1] for i in sorted(puntuados)]
	population = puntuados

	selected = puntuados[(len(puntuados)-pressure):]
	for i in range(len(population)-pressure):
		punto = random.randint(1,largo-1)
		padre = random.sample(selected, 2)

		population[i][:punto] = padre[0][:punto]
		population[i][punto:] = padre[1][punto:]
	return population

def mutation(population):
		for i in range(len(population)-pressure):
			if random.random() <= mutation_chance:
				punto = random.randint(0,largo-1)
				nuevo_valor = random.randint(1,9)
				while nuevo_valor == population[i][punto]:
					nuevo_valor = random.randint(1,9)
				population[i][punto] = nuevo_valor
		return population

population = crearPoblacion()
print("Poblacion inicial: \n%s"%(population))

for i in range(100):
	population = selection_and_reproduction(population)
	population = mutation(population)

print("\nPoblacion Final:\n%s"%(population))
print("\n\n")