selection.py 文件源码

def sigma_scaled(population: List[Genome], **kwargs) -> Iterator[PAIR_T]:
    try:
        assert len(population) > 1
    except AssertionError:
        raise TooFewIndividuals

    fitnesses = tuple(x.fitness for x in population)

    try:
        assert any(f > 0.0 for f in fitnesses)
    except AssertionError:
        return random_choice(population)

    sigma = stdev(fitnesses)

    average_fitness = mean(fitnesses)
    expected_value_func = lambda x: 1 if sigma == 0 else 1 + ((x - average_fitness) / (2 * sigma))
    sigma_sum = sum(expected_value_func(x) for x in fitnesses)
    scaling_func = lambda x: expected_value_func(x) / sigma_sum

    return roulette(population=population, scaling_func=scaling_func, **kwargs)