写遗传算法优化支持向量机的代码

好的，以下是一个简单的遗传算法优化支持向量机的Python代码示例：

import random
import numpy as np
from sklearn.svm import SVC

# 定义适应度函数
def fitness(individual, X_train, y_train):
    # 解码个体
    C = individual[0]
    gamma = individual[1]
    kernel = individual[2]
    
    # 构建SVM分类器
    clf = SVC(C=C, gamma=gamma, kernel=kernel)
    
    # 训练分类器并计算准确率
    clf.fit(X_train, y_train)
    accuracy = clf.score(X_train, y_train)
    
    return accuracy

# 定义遗传算法参数
POPULATION_SIZE = 50
NUM_GENERATIONS = 100
CROSSOVER_RATE = 0.8
MUTATION_RATE = 0.1

# 定义搜索空间
C_RANGE = (0.1, 100)
GAMMA_RANGE = (0.0001, 10)
KERNELS = ['linear', 'poly', 'rbf', 'sigmoid']

# 初始化种群
population = []
for i in range(POPULATION_SIZE):
    C = random.uniform(*C_RANGE)
    gamma = random.uniform(*GAMMA_RANGE)
    kernel = random.choice(KERNELS)
    individual = [C, gamma, kernel]
    population.append(individual)

# 遗传算法主循环
for generation in range(NUM_GENERATIONS):
    # 计算适应度
    fitness_scores = []
    for individual in population:
        fitness_score = fitness(individual, X_train, y_train)
        fitness_scores.append(fitness_score)
        
    # 选择
    selected_population = []
    for i in range(POPULATION_SIZE):
        # 轮盘赌选择
        probabilities = np.array(fitness_scores) / sum(fitness_scores)
        index = np.random.choice(range(POPULATION_SIZE), p=probabilities)
        selected_population.append(population[index])
    
    # 交叉
    crossover_population = []
    for i in range(POPULATION_SIZE):
        if random.random() < CROSSOVER_RATE:
            # 随机选择两个个体进行交叉
            parent1 = random.choice(selected_population)
            parent2 = random.choice(selected_population)
            child1 = parent1.copy()
            child2 = parent2.copy()
            # 随机选择一个基因进行交叉
            gene_index = random.randint(0, 2)
            child1[gene_index], child2[gene_index] = child2[gene_index], child1[gene_index]
            crossover_population += [child1, child2]
        else:
            crossover_population.append(selected_population[i])
    
    # 变异
    mutation_population = []
    for i in range(POPULATION_SIZE):
        if random.random() < MUTATION_RATE:
            # 随机选择一个个体进行变异
            parent = random.choice(crossover_population)
            child = parent.copy()
            # 随机选择一个基因进行变异
            gene_index = random.randint(0, 2)
            if gene_index == 0:
                child[gene_index] += random.choice([-1, 1]) * random.uniform(0, 10)
            elif gene_index == 1:
                child[gene_index] += random.choice([-1, 1]) * random.uniform(0, 1)
            else:
                child[gene_index] = random.choice(KERNELS)
            mutation_population.append(child)
        else:
            mutation_population.append(crossover_population[i])
    
    # 更新种群
    population = mutation_population

# 在测试集上评估最佳个体
best_individual = max(population, key=lambda x: fitness(x, X_train, y_train))
C = best_individual[0]
gamma = best_individual[1]
kernel = best_individual[2]
clf = SVC(C=C, gamma=gamma, kernel=kernel)
clf.fit(X_train, y_train)
test_accuracy = clf.score(X_test, y_test)
print('Test accuracy: %.2f%%' % (test_accuracy * 100))

需要注意的是，这只是一个简单的示例，实际的遗传算法优化支持向量机可能需要更复杂的适应度函数、选择、交叉和变异操作等。此外，还需要对搜索空间进行合理的选择和设置，以及对遗传算法的参数进行调优。