遗传算法python实例

下面是一个简单的遗传算法 Python 实例，用于寻找函数 f(x) = x^2 在 [-10, 10] 范围内的最大值：

import random

# 定义目标函数
def f(x):
    return x**2

# 遗传算法参数
pop_size = 20 # 种群大小
num_generations = 50 # 迭代次数
mutation_rate = 0.1 # 变异率

# 生成初始种群
pop = [random.uniform(-10, 10) for _ in range(pop_size)]

# 开始迭代
for _ in range(num_generations):
    # 计算适应度
    fitness = [f(x) for x in pop]
    
    # 选择
    selected = []
    for _ in range(pop_size):
        # 轮盘赌选择
        total_fitness = sum(fitness)
        r = random.uniform(0, total_fitness)
        acc_fitness = 0
        for i, x in enumerate(pop):
            acc_fitness += fitness[i]
            if acc_fitness > r:
                selected.append(x)
                break
    
    # 交叉
    offspring = []
    for i in range(pop_size):
        parent1 = random.choice(selected)
        parent2 = random.choice(selected)
        if parent1 != parent2:
            child = (parent1 + parent2) / 2
            offspring.append(child)
    
    # 变异
    for i in range(pop_size):
        if random.uniform(0, 1) < mutation_rate:
            offspring[i] += random.uniform(-1, 1)
    
    # 更新种群
    pop = offspring
    
# 寻找最优解
best_x = pop[0]
best_fitness = f(best_x)
for x in pop:
    fitness = f(x)
    if fitness > best_fitness:
        best_x = x
        best_fitness = fitness
        
print("最优解为：", best_x)
print("最大值为：", best_fitness)

该例子中，我们使用了轮盘赌选择、均值交叉和随机变异等基本遗传算法操作。在迭代 50 轮后，可以得到函数 f(x) 在 [-10, 10] 范围内的最大值。