自己举例

好的，以下是一个用遗传算法求解函数最小值的 Python 代码示例：

import random

# 目标函数，这里以 Rosenbrock 函数为例
def rosenbrock(x, y):
    return (1 - x) ** 2 + 100 * (y - x ** 2) ** 2

# 遗传算法求解函数最小值
def genetic_algorithm():
    # 设置参数
    num_population = 50 # 种群大小
    num_generation = 100 # 迭代次数
    mutation_rate = 0.01 # 变异率
    crossover_rate = 0.8 # 交叉率
    
    # 初始化种群
    population = []
    for i in range(num_population):
        x = random.uniform(-5, 5)
        y = random.uniform(-5, 5)
        fitness = rosenbrock(x, y)
        population.append({'x': x, 'y': y, 'fitness': fitness})
    
    # 迭代
    for i in range(num_generation):
        # 计算适应度
        fitness_sum = sum([p['fitness'] for p in population])
        for p in population:
            p['probability'] = p['fitness'] / fitness_sum
        
        # 选择
        new_population = []
        for j in range(num_population):
            p1 = random.choices(population, weights=[p['probability'] for p in population])[0]
            p2 = random.choices(population, weights=[p['probability'] for p in population])[0]
            
            # 交叉
            if random.random() < crossover_rate:
                x = (p1['x'] + p2['x']) / 2
                y = (p1['y'] + p2['y']) / 2
            else:
                x = p1['x']
                y = p1['y']
            
            # 变异
            if random.random() < mutation_rate:
                x += random.gauss(0, 0.1)
                y += random.gauss(0, 0.1)
            
            # 计算适应度
            fitness = rosenbrock(x, y)
            
            new_population.append({'x': x, 'y': y, 'fitness': fitness})
        
        population = new_population
    
    # 返回最优解
    return min(population, key=lambda p: p['fitness'])

# 测试
result = genetic_algorithm()
print(result)

这个示例程序使用遗传算法求解 Rosenbrock 函数最小值。在遗传算法中，首先初始化一个种群，然后不断迭代，每一次迭代都进行选择、交叉和变异操作。最后返回种群中的最优解作为结果。这个程序可以用来研究遗传算法的优化能力，并且可以修改目标函数和参数来进行不同的实验。