search.py 文件源码

def genetic_algorithm(population, fitness_fn, ngen=1000, pmut=0.0):
    """[Fig. 4.7]"""
    def reproduce(p1, p2):
        c = random.randrange(len(p1))
        return p1[:c] + p2[c:]

    for i in range(ngen):
        new_population = []
        for i in len(population):
            p1, p2 = random_weighted_selections(population, 2, fitness_fn)
            child = reproduce(p1, p2)
            if random.uniform(0,1) > pmut:
                child.mutate()
            new_population.append(child)
        population = new_population
    return argmax(population, fitness_fn)