遗传算法求二进制优化

import random

def fitness(bits):
    return sum(bits)

pop = [[random.randint(0, 1) for _ in range(8)] for _ in range(20)]
for _ in range(30):
    pop.sort(key=fitness, reverse=True)
    nxt = pop[:4]
    while len(nxt) < 20:
        a, b = random.sample(pop[:10], 2)
        cut = random.randint(1, 7)
        child = a[:cut] + b[cut:]
        if random.random() < 0.2:
            i = random.randrange(8)
            child[i] ^= 1
        nxt.append(child)
    pop = nxt
print(pop[0], fitness(pop[0]))