遗传算法和神经网络的代码

遗传算法是一种基于生物进化原理的优化搜索技术，主要用于解决复杂问题求解。其基本流程包括编码、选择、交叉和变异等步骤。在Python中，可以使用`deap`库来实现遗传算法，例如：

from deap import base, creator, tools

# 创建种群
creator.create("FitnessMax", base.Fitness, weights=(1.0,))
creator.create("Individual", list, fitness=creator.FitnessMax)

def eval(individual):
    # 这里是一个评估函数，根据个体的表现计算适应度值
    pass

toolbox = base.Toolbox()
toolbox.register("individual", tools.initRepeat, creator.Individual, choice)
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
toolbox.register("evaluate", eval)
toolbox.register("mate", tools.cxTwoPoint)
toolbox.register("mutate", tools.mutShuffleIndexes, indpb=0.05)
toolbox.register("select", tools.selTournament, tournsize=3)

pop = toolbox.population(n=100)  # 初始化一个包含100个个体的种群
for gen in range(50):  # 进行50代迭代
    offspring = toolbox.select(pop, len(pop))  # 选择优秀的个体繁殖
    offspring = [toolbox.clone(ind) for ind in offspring]  # 复制避免直接修改原种群
    for child1, child2 in zip(offspring[::2], offspring[1::2]):
        if random.random() < 0.5:
            toolbox.mate(child1, child2)  # 交叉
            del child1.fitness.values
            del child2.fitness.values
    for mutant in offspring:
        if random.random() < 0.1:
            toolbox.mutate(mutant)  # 变异
            del mutant.fitness.values
    invalids = [ind for ind in offspring if not ind.fitness.valid]
    fitnesses = map(toolbox.evaluate, invalids)
    for ind, fit in zip(invalids, fitnesses):
        ind.fitness.values = fit
    pop[:] = offspring  # 更新种群

神经网络代码通常使用深度学习框架如TensorFlow或PyTorch编写。这里是一个简单的全连接神经网络例子（使用PyTorch）：

import torch.nn as nn

class NeuralNetwork(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(NeuralNetwork, self).__init__()
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.relu = nn.ReLU()
        self.fc2 = nn.Linear(hidden_size, output_size)

    def forward(self, x):
        out = self.fc1(x)
        out = self.relu(out)
        out = self.fc2(out)
        return out

# 使用示例
model = NeuralNetwork(input_size=784, hidden_size=128, output_size=10)
input_data = torch.randn(1, 784)
output = model(input_data)