请提供一段python代码：基于ski-opt GA的遗传算法，用于优化已有的：20组gamma和nu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skopt GA的遗传算法用于优化已有的20组gamma和nu参数，适应度函数使用逆方差加权求和的Python代码：

import numpy as np
from skopt import gp_minimize
from skopt.space import Real
from skopt.utils import use_named_args
from sklearn.metrics import r2_score

# Define the 20-gamma and nu parameter space
param_space = [Real(low=0.01, high=1.0, name='gamma_{}'.format(i)) for i in range(20)]
param_space += [Real(low=0.1, high=1.0, name='nu_{}'.format(i)) for i in range(20)]

# Define the inverse variance weighting function
def weight_func(y_true, y_pred):
    return 1.0 / np.mean(np.var(y_true - y_pred, axis=0))

# Define the fitness function as the inverse variance weighted R-squared score
@use_named_args(param_space)
def fitness_func(**params):
    gamma_params = [params['gamma_{}'.format(i)] for i in range(20)]
    nu_params = [params['nu_{}'.format(i)] for i in range(20)]
    y_pred = evaluate_model(gamma_params, nu_params)
    fitness = weight_func(y_true, y_pred) * r2_score(y_true, y_pred)
    return -fitness

# Use skopt's GA optimizer to minimize the fitness function
result = gp_minimize(fitness_func, dimensions=param_space, n_calls=50)
best_params = result.x
best_fitness = -result.fun

print("Optimal gamma params: ", [best_params[i] for i in range(20)])
print("Optimal nu params: ", [best_params[i] for i in range(20,40)])
print("Best fitness: ", best_fitness)