请提供一段python代码：基于ski-opt GA的遗传算法，用于优化初始种群--20组参数，适应度函数使用逆方差加权求和的方式

以下是一段基于Scipy-Optimize的遗传算法的Python代码，用于优化20组参数，并使用逆方差加权求和的方式计算适应度函数：

import numpy as np
from scipy.optimize import differential_evolution

def fitness_func(x):
    # 先定义逆方差加权求和的函数
    def inv_cov_weighted_sum(x):
        weights = np.diag(1 / np.cov(x.T))
        means = np.mean(x, axis=0)
        x_centered = x - means
        return np.dot(x_centered.T, np.dot(weights, x_centered))

    # 计算适应度函数值
    num_params = 20
    fitness = np.zeros(10)
    for i in range(10):
        initial_population = np.random.uniform(-5, 5, size=(100, num_params))
        res = differential_evolution(inv_cov_weighted_sum, bounds=[(-5, 5)]*num_params, popsize=100, init=initial_population)
        fitness[i] = inv_cov_weighted_sum(res.x)
    return np.mean(fitness)

bounds = [(-5, 5)]*20
result = differential_evolution(fitness_func, bounds)
print(result.x)