如何使用 Python 堆实现遗传算法？

import heapq
import random

# 适应度函数，取值范围为 [0, 1]
def fitness(chromosome):
    target = "pidancode.com"
    score = 0
    for i in range(len(target)):
        if chromosome[i] == target[i]:
            score += 1
    return score / len(target)

# 生成一个随机的个体
def generate_individual(length):
    genes = []
    for i in range(length):
        genes.append(chr(random.randint(97, 122)))
    return "".join(genes)

# 交叉操作，利用随机点进行交叉，产生两个新的个体
def crossover(parent1, parent2):
    point = random.randint(0, len(parent1) - 1)
    child1 = parent1[:point] + parent2[point:]
    child2 = parent2[:point] + parent1[point:]
    return (child1, child2)

# 变异操作，将一个随机位置的基因替换为另一个随机的基因
def mutate(chromosome):
    genes = list(chromosome)
    point = random.randint(0, len(genes) - 1)
    genes[point] = chr(random.randint(97, 122))
    return "".join(genes)

# 初始化堆，生成一些随机的个体并计算它们的适应度
heap = []
for i in range(100):
    individual = generate_individual(len("pidancode.com"))
    fit = fitness(individual)
    heapq.heappush(heap, (-fit, individual))

# 进行迭代，每次从堆顶取出两个个体进行交叉和变异，并将产生的新个体插入到堆中
for i in range(1000):
    parent1 = heapq.heappop(heap)[1]
    parent2 = heapq.heappop(heap)[1]
    child1, child2 = crossover(parent1, parent2)
    child1 = mutate(child1)
    child2 = mutate(child2)
    fit1 = fitness(child1)
    fit2 = fitness(child2)
    heapq.heappush(heap, (-fit1, child1))
    heapq.heappush(heap, (-fit2, child2))

# 打印堆顶的最优解
print(heapq.heappop(heap)[1])