python遗传算法的优化过程是怎样的？

# 初始种群
pop = [Robot() for x in range(pop_size)]
results = []
 
# 执行进化
for i in tqdm(range(num_gen)):
    scores = np.zeros(pop_size)
 
    # 遍历所有机器人
    for idx, rob in enumerate(pop):
        # 运行垃圾收集模拟并计算拟合度
        score = rob.simulate(iter_per_sim, moves_per_iter)
        scores[idx] = score
 
    results.append([scores.mean(),scores.max()]) # 保存每一代的平均值和值
 
    best_robot = pop[scores.argmax()] # 保存最好的机器人
 
    # 限制那些能够交配的机器人的数量
    inds = np.argpartition(scores, -num_breeders)[-num_breeders:] # 基于拟合度得到顶级机器人的索引
    subpop = []
    for idx in inds:
        subpop.append(pop[idx])
    scores = scores[inds]
 
    # 平方并标准化
    norm_scores = (scores - scores.min()) ** 2
    norm_scores = norm_scores / norm_scores.sum()
 
    # 创造下一代机器人
    new_pop = []
    for child in range(pop_size):
        # 选择拟合度优秀的父母
        p1, p2 = np.random.choice(subpop, p=norm_scores, size=2, replace=False)
        new_pop.append(Robot(p1.dna, p2.dna))
 
pop = new_pop