遗传算法优化切比雪夫神经网络代码

遗传算法可以用于优化神经网络的参数，以下是使用遗传算法优化切比雪夫神经网络的Python代码示例：

import numpy as np
import random

# 定义网络结构
class ChebyshevNet:
    def __init__(self, input_size, hidden_size, output_size):
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.output_size = output_size

        # 随机初始化权重
        self.weights1 = np.random.randn(hidden_size, input_size)
        self.weights2 = np.random.randn(output_size, hidden_size)
        self.bias1 = np.random.randn(hidden_size)
        self.bias2 = np.random.randn(output_size)

    # 定义前向传播
    def forward(self, x):
        x = np.dot(self.weights1, x) + self.bias1
        x = np.maximum(x, 0)
        x = np.dot(self.weights2, x) + self.bias2
        return x

    # 定义适应度函数
    def fitness(self, data, labels):
        loss = 0
        for i in range(data.shape[0]):
            x = data[i]
            y = labels[i]
            y_pred = self.forward(x)
            loss += np.max(np.abs(y_pred - y))
        return -loss

# 定义遗传算法
class GeneticAlgorithm:
    def __init__(self, population_size, mutation_rate, elite_rate, tournament_size):
        self.population_size = population_size
        self.mutation_rate = mutation_rate
        self.elite_rate = elite_rate
        self.tournament_size = tournament_size

    # 生成随机种群
    def generate_population(self, input_size, hidden_size, output_size):
        population = []
        for i in range(self.population_size):
            net = ChebyshevNet(input_size, hidden_size, output_size)
            population.append(net)
        return population

    # 计算每个个体的适应度
    def evaluate_population(self, population, data, labels):
        fitnesses = []
        for net in population:
            fitness = net.fitness(data, labels)
            fitnesses.append(fitness)
        return fitnesses

    # 选择个体
    def select_population(self, population, fitnesses):
        selected = []
        while len(selected) < self.population_size:
            tournament = random.sample(range(len(population)), self.tournament_size)
            tournament_fitnesses = [fitnesses[i] for i in tournament]
            winner = tournament[np.argmax(tournament_fitnesses)]
            selected.append(population[winner])
        return selected

    # 交叉个体
    def crossover_population(self, population):
        offspring = []
        elite_size = int(self.elite_rate * self.population_size)
        elite = population[:elite_size]
        while len(offspring) < self.population_size - elite_size:
            parent1, parent2 = random.sample(population, 2)
            child1 = ChebyshevNet(parent1.input_size, parent1.hidden_size, parent1.output_size)
            child2 = ChebyshevNet(parent1.input_size, parent1.hidden_size, parent1.output_size)
            for i in range(parent1.hidden_size):
                if random.random() < 0.5:
                    child1.weights1[i] = parent1.weights1[i]
                    child2.weights1[i] = parent2.weights1[i]
                else:
                    child1.weights1[i] = parent2.weights1[i]
                    child2.weights1[i] = parent1.weights1[i]
            for i in range(parent1.output_size):
                if random.random() < 0.5:
                    child1.weights2[i] = parent1.weights2[i]
                    child2.weights2[i] = parent2.weights2[i]
                else:
                    child1.weights2[i] = parent2.weights2[i]
                    child2.weights2[i] = parent1.weights2[i]
            for i in range(parent1.hidden_size):
                if random.random() < 0.5:
                    child1.bias1[i] = parent1.bias1[i]
                    child2.bias1[i] = parent2.bias1[i]
                else:
                    child1.bias1[i] = parent2.bias1[i]
                    child2.bias1[i] = parent1.bias1[i]
            for i in range(parent1.output_size):
                if random.random() < 0.5:
                    child1.bias2[i] = parent1.bias2[i]
                    child2.bias2[i] = parent2.bias2[i]
                else:
                    child1.bias2[i] = parent2.bias2[i]
                    child2.bias2[i] = parent1.bias2[i]
            offspring.append(child1)
            offspring.append(child2)
        return elite + offspring

    # 变异个体
    def mutate_population(self, population):
        for net in population:
            for i in range(net.hidden_size):
                if random.random() < self.mutation_rate:
                    net.weights1[i] += np.random.randn(net.input_size) * 0.1
            for i in range(net.output_size):
                if random.random() < self.mutation_rate:
                    net.weights2[i] += np.random.randn(net.hidden_size) * 0.1
            for i in range(net.hidden_size):
                if random.random() < self.mutation_rate:
                    net.bias1[i] += np.random.randn() * 0.1
            for i in range(net.output_size):
                if random.random() < self.mutation_rate:
                    net.bias2[i] += np.random.randn() * 0.1
        return population

    # 进化种群
    def evolve_population(self, population, data, labels):
        fitnesses = self.evaluate_population(population, data, labels)
        population = self.select_population(population, fitnesses)
        population = self.crossover_population(population)
        population = self.mutate_population(population)
        return population

# 定义数据集
data = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
labels = np.array([[0], [1], [1], [0]])

# 定义遗传算法参数
input_size = data.shape[1]
hidden_size = 10
output_size = labels.shape[1]
population_size = 100
mutation_rate = 0.1
elite_rate = 0.2
tournament_size = 5

# 生成随机种群
ga = GeneticAlgorithm(population_size, mutation_rate, elite_rate, tournament_size)
population = ga.generate_population(input_size, hidden_size, output_size)

# 进化种群
for i in range(100):
    population = ga.evolve_population(population, data, labels)

# 选出最优个体
fitnesses = ga.evaluate_population(population, data, labels)
best_index = np.argmax(fitnesses)
best_net = population[best_index]

# 打印最优个体的预测结果
for i in range(data.shape[0]):
    x = data[i]
    y = labels[i]
    y_pred = best_net.forward(x)
    print("Input:", x, "Actual output:", y, "Predicted output:", y_pred)

在这个示例中，我们使用遗传算法优化一个切比雪夫神经网络来解决异或问题。我们定义了一个`ChebyshevNet`类来表示切比雪夫神经网络，它包含一个前向传播函数和一个适应度函数。我们还定义了一个`GeneticAlgorithm`类来实现遗传算法，它包含生成随机种群、计算适应度、选择、交叉和变异等操作。最后，我们使用遗传算法来进化种群并找到最优个体，然后使用最优个体来预测输入数据的输出。