遗传算法中的优化参数问题

import random

# 种群类
class Population:
    def __init__(self, size):
        self.size = size
        self.individuals = []

        for _ in range(size):
            individual = Individual()
            self.individuals.append(individual)

    # 选择父代个体
    def select_parents(self):
        parents = []

        for _ in range(size):
            parent = random.choice(self.individuals)
            parents.append(parent)

        return parents

    # 交叉和变异
    def crossover_and_mutation(self, parents):
        new_generation = []

        for _ in range(size):
            parent1 = random.choice(parents)
            parent2 = random.choice(parents)

            child = parent1.crossover(parent2)
            child.mutation()

            new_generation.append(child)

        return new_generation

# 个体类
class Individual:
    def __init__(self):
        self.parameters = []

        for _ in range(10):
            parameter = random.uniform(0, 1)
            self.parameters.append(parameter)

    # 交叉操作
    def crossover(self, other):
        child = Individual()

        for i in range(10):
            if random.random() < 0.5:
                child.parameters[i] = self.parameters[i]
            else:
                child.parameters[i] = other.parameters[i]

        return child

    # 变异操作
    def mutation(self):
        for i in range(10):
            if random.random() < mutation_rate:
                self.parameters[i] = random.uniform(0, 1)