Ejemplo n.º 1
0
def ea(SIGMA=SIGMA):
    # initialize
    world = World(fitness=fitness)
    world.population = IntListPopulation(length=MU, gene_size=GENE_SIZE)

    # iterate over generations
    for generation_count in range(MAX_GENERATIONS):

        # log this generation's population to stdout
        print("%s: %s" % (generation_count, world.population))

        # if we reached a sufficiently good solution, stop
        if min(world.scores) < MIN_FITNESS:
            break

        # select rho fittest individuals as parents
        parents = select(world=world, rho=RHO)

        # generate children by crossing over the parents
        children_original = []
        while len(children_original) < LAMBDA:
            children_original += crossover(population=parents)

        # mutate children
        children = [mutate(individual=individual, mu=0, sigma=SIGMA)
                    for individual in children_original]

        # self-adapt mutation strenght using Rechenberg's success rule
        SIGMA = rechenberg(children_original=children_original,
                           children_mutated=children, fitness=world.fitness,
                           a=A_RECHENBERG, sigma=SIGMA)

        if MODE == '+':
            # extend population with children
            world.population += Population.from_individuals(
                individuals=children)
        elif MODE == ',':
            # replace population with children
            world.population = Population.from_individuals(
                individuals=children)

        # select fittest mu individuals for next generation
        world.population = select(rho=MU, world=world)

    # log best individual
    print("Best individual is %s with a score of %s." %
          (select(world=world, rho=1)[0], min(world.scores)))
Ejemplo n.º 2
0
def test():
    WORLD = World(fitness=fitness)
    WORLD.population = IntListPopulation(length=10)
    rho = 2
    PARENTS = select_fittest(WORLD, rho=rho)
    assert len(PARENTS) == rho
    assert PARENTS[0].gene[0] == 0
    assert PARENTS[1].gene[0] == 0