遗传算法优化CNN的代码。

以下是使用遗传算法优化CNN代码的一些示例步骤：

1. 定义CNN模型，包括卷积层、池化层和全连接层。可以使用Keras或TensorFlow等深度学习框架来定义模型。

2. 定义超参数空间，包括卷积核大小、卷积核数量、池化大小、全连接层神经元数量等。

3. 定义适应度函数，用于评估每个个体的性能。可以使用交叉验证等方法进行评估。

4. 使用遗传算法进行优化，包括选择、交叉和变异操作。可以使用Python中的遗传算法库，如DEAP等。

5. 重复步骤3和4，直到达到最优解或满足停止条件。

下面是一个简单的遗传算法优化CNN的代码示例：

import random
import numpy as np
from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
from keras.models import Sequential
from keras.datasets import mnist

# 定义CNN模型
def create_model(params):
    model = Sequential()
    model.add(Conv2D(params['filters'], (params['kernel_size'], params['kernel_size']), activation='relu', input_shape=(28, 28, 1)))
    model.add(MaxPooling2D(pool_size=(params['pool_size'], params['pool_size'])))
    model.add(Flatten())
    model.add(Dense(params['dense_units'], activation='relu'))
    model.add(Dense(10, activation='softmax'))
    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
    return model

# 定义适应度函数
def fitness(params):
    (X_train, y_train), (X_test, y_test) = mnist.load_data()
    X_train = X_train.reshape(X_train.shape[0], 28, 28, 1)
    X_test = X_test.reshape(X_test.shape[0], 28, 28, 1)
    X_train = X_train.astype('float32') / 255
    X_test = X_test.astype('float32') / 255
    y_train = keras.utils.to_categorical(y_train, 10)
    y_test = keras.utils.to_categorical(y_test, 10)
    model = create_model(params)
    model.fit(X_train, y_train, batch_size=128, epochs=5, validation_data=(X_test, y_test))
    score = model.evaluate(X_test, y_test, verbose=0)
    return score[1]

# 定义超参数空间
params_space = {
    'filters': [16, 32, 64],
    'kernel_size': [3, 5, 7],
    'pool_size': [2, 3],
    'dense_units': [64, 128, 256]
}

# 定义遗传算法参数
population_size = 10
num_generations = 10
mutation_rate = 0.1

# 初始化种群
population = []
for i in range(population_size):
    individual = {}
    for key in params_space:
        individual[key] = random.choice(params_space[key])
    population.append(individual)

# 遗传算法优化
for generation in range(num_generations):
    # 计算适应度
    fitness_scores = []
    for individual in population:
        fitness_scores.append(fitness(individual))
    best_fitness = max(fitness_scores)
    print('Generation %d - Best fitness: %f' % (generation, best_fitness))
    # 选择操作
    parents = []
    for i in range(population_size):
        parent1 = population[np.random.randint(0, population_size)]
        parent2 = population[np.random.randint(0, population_size)]
        if fitness(parent1) > fitness(parent2):
            parents.append(parent1)
        else:
            parents.append(parent2)
    # 交叉操作
    offspring = []
    for i in range(population_size):
        child = {}
        parent1 = parents[np.random.randint(0, population_size)]
        parent2 = parents[np.random.randint(0, population_size)]
        for key in params_space:
            if np.random.rand() < 0.5:
                child[key] = parent1[key]
            else:
                child[key] = parent2[key]
        offspring.append(child)
    # 变异操作
    for i in range(population_size):
        individual = offspring[i]
        for key in params_space:
            if np.random.rand() < mutation_rate:
                individual[key] = random.choice(params_space[key])
    # 更新种群
    population = offspring

以上代码使用遗传算法优化MNIST数据集上的CNN模型。在`params_space`中定义了超参数空间，包括卷积核大小、卷积核数量、池化大小和全连接层神经元数量。`create_model`函数用于创建CNN模型，`fitness`函数用于计算每个个体的适应度。在遗传算法循环中，包括选择、交叉和变异操作。每个个体的适应度通过交叉验证进行评估。