python实现EDA算法，给出完整的代码

好的，以下是一个完整的Python实现的EDA算法的代码示例：

import numpy as np
from scipy.stats import truncnorm

# 定义初始化种群函数
def initialize_population(pop_size, n_vars, bounds):
    pop = []
    for i in range(pop_size):
        individual = []
        for j in range(n_vars):
            a, b = bounds[j]
            r = truncnorm.rvs(a, b)
            individual.append(r)
        pop.append(individual)
    return np.array(pop)

# 定义适应度函数
def fitness_func(individual):
    # 这里假设适应度函数为个体各变量值的平方和
    return -np.sum(np.square(individual))

# 定义选择父代函数
def select_parents(population, fitness, n_parents):
    fitness = fitness / np.sum(fitness)
    parents_idx = np.random.choice(np.arange(len(population)), size=n_parents, replace=False, p=fitness)
    return population[parents_idx]

# 定义生成新个体函数
def generate_offspring(parents, n_offspring):
    n_parents, n_vars = parents.shape
    offspring = []
    for i in range(n_offspring):
        p1 = parents[np.random.randint(n_parents)]
        p2 = parents[np.random.randint(n_parents)]
        child = []
        for j in range(n_vars):
            if np.random.rand() < 0.5:
                child.append(p1[j])
            else:
                child.append(p2[j])
        offspring.append(child)
    return np.array(offspring)

# 定义运行EDA算法函数
def eda(pop_size, n_vars, bounds, fitness_func, n_generations):
    population = initialize_population(pop_size, n_vars, bounds)
    for i in range(n_generations):
        fitness = evaluate(population, fitness_func)
        parents = select_parents(population, fitness, pop_size // 2)
        offspring = generate_offspring(parents, pop_size)
        population = np.vstack((population, offspring))
        fitness = evaluate(population, fitness_func)
        elite_idx = np.argsort(fitness)[-pop_size:]
        population = population[elite_idx]
    return population[np.argmax(evaluate(population, fitness_func))]

# 定义计算适应度函数
def evaluate(population, fitness_func):
    return np.array([fitness_func(individual) for individual in population])

# 测试运行EDA算法
pop_size = 100 # 种群大小
n_vars = 10 # 变量数量
bounds = [(-2, 2)] * n_vars # 变量范围
n_generations = 100 # 迭代次数

best_individual = eda(pop_size, n_vars, bounds, fitness_func, n_generations)
print("最优解：", best_individual)
print("最优适应度：", -fitness_func(best_individual))

上述代码中，假设适应度函数为个体各变量值的平方和，可根据实际问题进行修改。在测试运行EDA算法时，可以根据具体问题设置种群大小、变量数量、变量范围和迭代次数等参数。