生成一段使用遗传算法的人工神经网络代码

遗传算法可以用于优化神经网络的权重和结构，下面是一个使用遗传算法训练神经网络的示例代码：

import random
import numpy as np

class NeuroEvolution:
    def __init__(self, input_size, output_size, pop_size):
        self.input_size = input_size
        self.output_size = output_size
        self.pop_size = pop_size

        self.population = []
        for i in range(pop_size):
            chromosome = []
            chromosome.append(np.random.randint(1, 10))  # 随机生成输入层节点数
            chromosome.append(np.random.randint(1, 10))  # 随机生成隐藏层节点数
            chromosome.append(np.random.randint(1, 10))  # 随机生成隐藏层节点数
            chromosome.append(np.random.randn(chromosome[0], chromosome[1]))  # 随机生成输入层到第一层隐藏层的权重
            chromosome.append(np.random.randn(chromosome[1], chromosome[2]))  # 随机生成第一层隐藏层到第二层隐藏层的权重
            chromosome.append(np.random.randn(chromosome[2], output_size))  # 随机生成第二层隐藏层到输出层的权重
            self.population.append(chromosome)

    def fitness(self, chromosome, X, y):
        input_size, hidden_size1, hidden_size2, w1, w2, w3 = chromosome
        z1 = np.dot(X, w1)
        a1 = np.tanh(z1)
        z2 = np.dot(a1, w2)
        a2 = np.tanh(z2)
        z3 = np.dot(a2, w3)
        output = self.softmax(z3)

        loss = -np.sum(y * np.log(output)) / len(y)
        return 1 / (1 + loss)

    def softmax(self, x):
        e_x = np.exp(x - np.max(x))
        return e_x / e_x.sum(axis=1, keepdims=True)

    def crossover(self, parent1, parent2):
        child = []
        for i in range(len(parent1)):
            if isinstance(parent1[i], int):
                child.append(parent1[i] if random.random() < 0.5 else parent2[i])
            else:
                alpha = np.random.uniform(-0.1, 1.1, parent1[i].shape)
                child.append(alpha * parent1[i] + (1 - alpha) * parent2[i])
        return child

    def mutate(self, chromosome, mutation_rate):
        for i in range(len(chromosome)):
            if isinstance(chromosome[i], int):
                chromosome[i] += np.random.randint(-1, 2)
            else:
                chromosome[i] += np.random.normal(0, mutation_rate, chromosome[i].shape)
        return chromosome

    def evolve(self, X, y, elitism, mutation_rate):
        fitnesses = [self.fitness(chromosome, X, y) for chromosome in self.population]
        sorted_indices = np.argsort(fitnesses)[::-1]

        new_population = []
        if elitism:
            new_population.append(self.population[sorted_indices[0]])
        while len(new_population) < self.pop_size:
            parent1 = self.population[sorted_indices[np.random.randint(0, self.pop_size)]]
            parent2 = self.population[sorted_indices[np.random.randint(0, self.pop_size)]]
            child = self.crossover(parent1, parent2)
            if random.random() < mutation_rate:
                child = self.mutate(child, mutation_rate)
            new_population.append(child)

        self.population = new_population

以上是一个简单的遗传算法的神经网络的代码示例，其中包括了初始化种群、计算适应度、交叉和变异等方法。需要注意的是，这里使用了tanh和softmax作为激活函数，可以根据实际情况进行调整。