“把结果输入到训练好的遗传算法优化BP神经网络模型，该模型输出对应优化后的参数”请给出代码

将训练好的遗传算法应用于优化BP神经网络模型的过程通常涉及几个步骤，这里提供一个简化版的Python代码示例，使用scikit-learn库中的`NeuralNetworkClassifier`作为基础模型，并结合遗传算法（如DEAP库）来进行参数优化：

# 导入必要的库
from deap import base, creator, tools
import numpy as np
from sklearn.neural_network import MLPClassifier

# 定义遗传算法基本结构
creator.create("FitnessMax", base.Fitness, weights=(1.0,))
creator.create("Individual", list, fitness=creator.FitnessMax)

def eval_fitness(individual):
    # 初始化神经网络模型并设置个体对应的参数
    nn = MLPClassifier(hidden_layer_sizes=tuple(individual), random_state=42)
    nn.fit(X_train, y_train)  # 假设X_train和y_train是你的训练数据
    # 计算模型性能指标，比如准确率、F1分数等
    score = nn.score(X_test, y_test)
    return score,

toolbox = base.Toolbox()
toolbox.register("attr_hidden_layers", tuple, np.random.randint(5, 10, size=2))  # 隐藏层大小范围
toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_hidden_layers)
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
toolbox.register("evaluate", eval_fitness)
toolbox.register("mate", tools.cxTwoPoint)
toolbox.register("mutate", tools.mutShuffleIndexes, indpb=0.05)
toolbox.register("select", tools.selTournament, tournsize=3)

# 主循环 - 遗传算法优化
pop = toolbox.population(n=50)  # 创建初始种群
for gen in range(100):  # 设定迭代次数
    offspring = toolbox.select(pop, len(pop))
    offspring = [toolbox.clone(ind) for ind in offspring]  # 复制个体以避免修改原始种群
    offspring = [toolbox.mate(ind1, ind2) for ind1, ind2 in zip(offspring[::2], offspring[1::2])]
    offspring = [toolbox_mutate(ind) for ind in offspring]
    
    invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
    fitnesses = map(toolbox.evaluate, invalid_ind)
    for ind, fit in zip(invalid_ind, fitnesses):
        ind.fitness.values = fit
    
    pop[:] = offspring  # 更新种群
    best_ind = tools.selBest(pop, 1)[0]  # 获取当前最优个体

# 最终得到优化后的神经网络参数
best_hidden_layers = best_ind.fitness.weights  # 这里假设权重等于隐藏层大小