遗传算法 pytorch

遗传算法是一种优化算法，它通过模拟生物进化的过程来寻找问题的最优解。在遗传算法中，解决方案被表示为一组基因，每个基因都对应于问题的一个变量。遗传算法使用基因交叉和变异等操作来生成新的解决方案，同时通过适应度函数来评估每个解决方案的质量。

在PyTorch中，可以使用遗传算法优化模型的权重和超参数。首先，通过定义适应度函数来评估模型性能。然后，将模型的权重和超参数表示为基因，并定义基因的交叉和变异操作。最后，使用遗传算法来搜索最优的模型权重和超参数。

以下是使用PyTorch实现遗传算法的示例代码：

import random
import torch
from torch import nn
from torch.utils.data import DataLoader

# 定义模型
class Model(nn.Module):
    def __init__(self, input_size, output_size, hidden_size):
        super(Model, self).__init__()
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.fc2 = nn.Linear(hidden_size, output_size)
        self.relu = nn.ReLU()

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

# 定义适应度函数
def fitness_function(model, data_loader):
    criterion = nn.CrossEntropyLoss()
    optimizer = torch.optim.SGD(model.parameters(), lr=0.1)
    for inputs, targets in data_loader:
        outputs = model(inputs)
        loss = criterion(outputs, targets)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
    return 1 / loss.item()

# 定义基因编码和解码函数
def encode(model):
    genes = []
    for param in model.parameters():
        genes.append(param.view(-1))
    return torch.cat(genes)

def decode(genes, model):
    index = 0
    for param in model.parameters():
        param_num = param.numel()
        param.data = genes[index:index+param_num].view(param.size())
        index += param_num

# 定义遗传算法操作
def crossover(parent1, parent2):
    crossover_point = random.randint(0, len(parent1)-1)
    child1 = torch.cat([parent1[:crossover_point], parent2[crossover_point:]])
    child2 = torch.cat([parent2[:crossover_point], parent1[crossover_point:]])
    return child1, child2

def mutation(genes, mutation_rate):
    for i in range(len(genes)):
        if random.random() < mutation_rate:
            genes[i] += torch.randn(genes[i].size())
    return genes

# 设置参数
input_size = 784
hidden_size = 128
output_size = 10
population_size = 50
mutation_rate = 0.1
num_generations = 100

# 准备数据
train_data = DataLoader(...)
test_data = DataLoader(...)

# 初始化种群
population = []
for i in range(population_size):
    model = Model(input_size, output_size, hidden_size)
    genes = encode(model)
    population.append(genes)

# 进化
for generation in range(num_generations):
    # 计算适应度
    fitness_scores = []
    for genes in population:
        model = Model(input_size, output_size, hidden_size)
        decode(genes, model)
        fitness_score = fitness_function(model, train_data)
        fitness_scores.append(fitness_score)

    # 选择父代
    parent1_idx = fitness_scores.index(max(fitness_scores))
    parent2_idx = random.choice([i for i in range(population_size) if i != parent1_idx])
    parent1 = population[parent1_idx]
    parent2 = population[parent2_idx]

    # 交叉和变异
    child1, child2 = crossover(parent1, parent2)
    child1 = mutation(child1, mutation_rate)
    child2 = mutation(child2, mutation_rate)

    # 替换最差的个体
    min_fitness_idx = fitness_scores.index(min(fitness_scores))
    population[min_fitness_idx] = child1

    # 替换次差的个体
    fitness_scores[min_fitness_idx] = 1 / fitness_function(decode(child2, Model(input_size, output_size, hidden_size)), train_data)
    second_min_fitness_idx = fitness_scores.index(min(fitness_scores))
    population[second_min_fitness_idx] = child2

# 选择最优解
best_model = Model(input_size, output_size, hidden_size)
decode(population[fitness_scores.index(max(fitness_scores))], best_model)

# 测试模型
correct = 0
total = 0
with torch.no_grad():
    for inputs, targets in test_data:
        outputs = best_model(inputs)
        _, predicted = torch.max(outputs.data, 1)
        total += targets.size(0)
        correct += (predicted == targets).sum().item()

print('Accuracy: {:.2f}%'.format(100 * correct / total))

在上面的示例代码中，我们使用遗传算法来搜索最优的神经网络权重和超参数，以分类MNIST数据集中的手写数字。在每一代中，我们计算种群中每个个体的适应度，并使用基因交叉和变异操作生成新的个体。然后，我们将新的个体替换种群中最差的个体和次差的个体。最后，我们选择具有最高适应度的个体作为最优解，并使用它来分类测试数据集。