an example of a genetic algoritm

#!/usr/bin/env python

import numpy as np
import time, random

class GeneticALG():
    def __init__(self, chromosome_size, population_size, fitness_test, crossover=0.7, mutation=0.001, elite_pool=0.10):
        self.chromosome_size = chromosome_size
        self.crossover       = crossover
        self.mutation        = mutation
        self.elite_pool      = elite_pool
        self.population_size = population_size
        self.fitness_test    = fitness_test
        self.population      = list()
        self.generation      = 0
        np.random.seed([int(time.time() * 1e9) % 4294967296])
        random.seed(time.time())
        for i in xrange(population_size):
            self.population.append([
                np.random.choice([0, 1], size=(chromosome_size,)),
                0
             ])


    def test_fitness(self):
        # test fitness
        for c in self.population:
            c[1] = self.fitness_test(c[0])

    def breed(self):
        # choose two mates
        op = sorted(self.population, key=lambda x: x[1], reverse=True)
        for i in xrange(int(self.population_size * self.crossover)):
            male   = random.randint(0, self.population_size - 1)
            female = random.choice(op[:int(self.population_size * self.elite_pool)])
            splice = random.randint(0, self.chromosome_size - 1)

            for p in xrange(self.chromosome_size - splice):
                index = p + splice
                self.population[male][0][index] = female[0][index]

        # mutate
        for i in xrange(int(self.population_size * self.mutation)):
            cancer   = random.randint(0, self.chromosome_size - 1)
            victim   = random.randint(0, self.population_size - 1)
            self.population[victim][0][cancer] = 0 if self.population[victim][0][cancer] else 1

        # test fitness
        self.test_fitness()
        self.generation += 1

if __name__ == "__main__":
    from numpy import array_equal
    def fitness(x):
        r = 0
        if np.array_equal(x & np.array([0,0,0,0,0,0,0,0,1,0,1,1]), np.array([0,0,0,0,0,0,0,0,1,0,1,1])):
            r += 1
        if np.array_equal(x & np.array([0,1,0,0,1,0,0,0,0,0,0,0]), np.array([0,1,0,0,1,0,0,0,0,0,0,0])):
            r += 2
        if np.array_equal(x & np.array([0,0,0,0,0,1,1,0,0,0,0,0]), np.array([0,0,0,0,0,1,1,0,0,0,0,0])):
            r += 3
        if np.array_equal(x , np.array([0,1,0,0,1,1,1,0,1,0,1,1])):
            r *= 7
        return r

    species = GeneticALG(12, 500, fitness)

    print "[*] Starting population generated:"

    while True:
        print "[+] Generation %i breeding:" % species.generation
        species.breed()
        print "[+] Top 5 census:"
        census = sorted(species.population, key=lambda x: x[1], reverse=True)
        for i in xrange(5):
            print "\t%s" % census[i]
            if census[i][1] > 6:
                exit(1)