请提供一段python代码：基于ski-opt GA的遗传算法，传入20组gamma和nu参数进行遗传算法优化，适应度函数已知

以下是一段基于skopt库的遗传算法代码示例，其中使用了 GA (genetic algorithm) 优化器，并传入 20 组 gamma 和 nu 参数，同时假设适应度函数为 "fitness_func"。

from skopt import gp_minimize
from skopt.utils import use_named_args
from skopt.space import Real, Space
from skopt.plots import plot_convergence

import numpy as np

# Define the search space
space = Space([
    Real(0.1, 1.0, name='gamma'),
    Real(0.1, 1.0, name='nu')
])

# Define the fitness function
def fitness_func(x):
    # Compute the fitness based on x, which contains the gamma and nu values
    # ...

# Define the optimization objective with genetic algorithm
@use_named_args(space)
def objective(**params):
    return fitness_func([params['gamma'], params['nu']])

# Set up the genetic algorithm optimizer
res_gp = gp_minimize(objective, space, n_calls=100, n_random_starts=10,
                     acq_func='EI', xi=0.01, kappa=1.96, x0=None,
                     y0=None, random_state=None, verbose=True)

# Print the results
print("Best fitness value: {0:.4f}".format(res_gp.fun))
print("Best gamma and nu values: {}".format(res_gp.x))

# Plot the convergence of the optimization
fig, ax = plt.subplots()
ax.plot(np.minimum.accumulate(res_gp.func_vals), 'b-', label='GA')
ax.set_xlabel('Iteration')
ax.set_ylabel('Best function value')
plt.show()

注意：此代码示例只是演示如何使用 skopt 库中的遗传算法优化器，并传入参数。适应度函数 "fitness_func" 需要由用户自己定义，并根据具体情况编写。