请提供一段python代码:GA 优化器,优化20组gamma和nu的参数,适应度函数为逆方差加权求和(不要把种群放在适应度函数里面)
时间: 2024-05-15 10:18:22 浏览: 10
以下是一段使用遗传算法优化20组参数的示例代码,适应度函数采用逆方差加权求和:
```
import numpy as np
from scipy.optimize import minimize
import random
# 适应度函数,返回逆方差加权求和的结果
def fitness(params, X, y):
y_pred = np.dot(X, params)
mse = np.mean((y - y_pred)**2)
weights = 1.0 / np.var(y - y_pred)
return np.sum(weights * mse)
# 遗传算法优化器
def genetic_algorithm(X, y, population_size, generations, mutation_prob, elite_percent):
# 生成初始种群
population = np.zeros((population_size, X.shape[1]))
for i in range(population_size):
population[i] = np.random.normal(0, 1, X.shape[1])
# 开始遗传算法迭代
for gen in range(generations):
# 计算每个个体的适应度
fitness_values = []
for i in range(population_size):
fitness_values.append(fitness(population[i], X, y))
# 选择精英个体
elite_num = int(population_size * elite_percent)
elite_indices = np.argsort(fitness_values)[:elite_num]
elite_population = population[elite_indices]
# 生成新的种群
new_population = np.zeros((population_size, X.shape[1]))
for i in range(population_size):
# 随机选择两个父代
parents = random.choices(population, weights=fitness_values, k=2)
# 交叉操作
crossover_point = np.random.randint(0, X.shape[1])
child = np.concatenate((parents[0][:crossover_point], parents[1][crossover_point:]))
# 突变操作
if np.random.uniform(0, 1) < mutation_prob:
mutation_point = np.random.randint(0, X.shape[1])
child[mutation_point] += np.random.normal(0, 1)
new_population[i] = child
# 合并精英个体和新的种群
population = np.concatenate((elite_population, new_population))
# 返回最优个体
fitness_values = []
for i in range(population_size):
fitness_values.append(fitness(population[i], X, y))
best_index = np.argmin(fitness_values)
return population[best_index]
# 生成一些样本数据
X = np.random.randn(100, 20)
true_params = np.random.randn(20)
y = np.dot(X, true_params)
# 使用遗传算法优化20组参数
best_params = genetic_algorithm(X, y, population_size=100, generations=100, mutation_prob=0.1, elite_percent=0.1)
# 输出最优参数
print(best_params)
```