遗传算法优化支持向量机python代码

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 import svm
from sklearn.datasets import make_classification

# 生成数据集
X, y = make_classification(n_samples=1000, n_features=10, n_informative=5, n_redundant=0, random_state=1)

# 定义适应度函数，即SVM的准确率
def fitness(individual, X, y):
    clf = svm.SVC(C=individual[0], kernel=individual[1], gamma=individual[2])
    clf.fit(X, y)
    accuracy = clf.score(X, y)
    return accuracy

# 定义遗传算法
def genetic_algorithm(population_size, generations, mutation_rate, X, y):
    # 初始化种群
    population = []
    for i in range(population_size):
        individual = [random.uniform(0.1, 10), random.choice(['linear', 'rbf']), random.uniform(0.1, 1)]
        population.append(individual)
    # 进化
    for generation in range(generations):
        # 计算适应度
        fitness_scores = []
        for individual in population:
            fitness_scores.append(fitness(individual, X, y))
        # 选择
        parents = []
        for i in range(population_size):
            parent1 = population[fitness_scores.index(max(fitness_scores))]
            fitness_scores[fitness_scores.index(max(fitness_scores))] = -1
            parent2 = population[fitness_scores.index(max(fitness_scores))]
            fitness_scores[fitness_scores.index(max(fitness_scores))] = -1
            parents.append([parent1, parent2])
        # 交叉
        offspring = []
        for i in range(population_size):
            child = []
            for j in range(len(parents[i][0])):
                if random.random() < 0.5:
                    child.append(parents[i][0][j])
                else:
                    child.append(parents[i][1][j])
            offspring.append(child)
        # 变异
        for i in range(population_size):
            for j in range(len(offspring[i])):
                if random.random() < mutation_rate:
                    if j == 0:
                        offspring[i][j] = random.uniform(0.1, 10)
                    elif j == 1:
                        offspring[i][j] = random.choice(['linear', 'rbf'])
                    else:
                        offspring[i][j] = random.uniform(0.1, 1)
        # 更新种群
        population = offspring
    # 返回最优解
    max_fitness = 0
    for individual in population:
        fitness_score = fitness(individual, X, y)
        if fitness_score > max_fitness:
            max_fitness = fitness_score
            best_individual = individual
    return best_individual

# 运行遗传算法
best_individual = genetic_algorithm(population_size=50, generations=100, mutation_rate=0.1, X=X, y=y)

# 输出最优解
print('Best individual:', best_individual)

此代码使用遗传算法来搜索SVM的最佳参数（C、kernel和gamma），以最大化SVM的准确率。它首先生成一个包含1000个样本和10个特征的分类数据集，然后定义适应度函数来计算SVM的准确率。接下来，它使用遗传算法来搜索最佳参数，其中种群大小为50，进化代数为100，变异率为0.1。最后，它输出找到的最佳参数。