遗传算法python代码应用实例

遗传算法在Python中的应用通常用于优化问题，比如函数极值搜索。下面是一个简化版的遗传算法用于求解单峰函数的极值的Python代码示例[^1]：

import numpy as np
from random import uniform

# 初始化参数
def initialize_population(pop_size, problem_dim):
    return [np.random.rand(problem_dim) for _ in range(pop_size)]

# 函数适应度评估
def fitness_function(solution):
    # 假设我们正在寻找一个函数f(x)的最小值
    # 对于单峰函数，越接近峰值，适应度越好
    return -solution**2

# 选择操作
def selection(population, fitness):
    elite_count = len(population) // 10  # 精英保留比例
    sorted_indices = np.argsort(fitness)[:elite_count]  # 选取适应度最高的个体
    selected = population[sorted_indices]
    remaining = population[~np.isin(np.arange(len(population)), sorted_indices)]
    return np.concatenate([selected, remaining])

# 交叉操作
def crossover(parents, crossover_rate):
    child_size = parents.shape[0]
    cross_points = np.random.choice(child_size, 2, replace=False)
    parent1, parent2 = parents[cross_points]
    offspring = np.zeros_like(parents)
    offspring[(cross_points[0]:child_size), :] = parent1[(cross_points[1]:):]
    offspring[(cross_points[1]:), :] = parent2[:cross_points[0]]
    return offspring

# 变异操作
def mutation(children, mutation_rate, problem_dim):
    mutated_children = children.copy()
    for i in range(len(mutated_children)):
        if uniform(0, 1) < mutation_rate:
            mutated_children[i] += uniform(-1, 1) * (mutated_children[i].max() - mutated_children[i].min())
    return mutated_children

# 主循环
def genetic_algorithm(pop_size, generations, problem_dim, crossover_rate, mutation_rate):
    population = initialize_population(pop_size, problem_dim)
    for _ in range(generations):
        fitness = np.array([fitness_function(sol) for sol in population])
        population = selection(population, fitness)
        population = crossover(population, crossover_rate)
        population = mutation(population, mutation_rate, problem_dim)
    best_solution = population[np.argmin(fitness_function(population))]
    return best_solution, fitness_function(best_solution)

# 示例调用
best_solution, fitness = genetic_algorithm(100, 100, 1, 0.8, 0.01)
print(f"Best solution found: {best_solution}, with fitness: {fitness}")