遗传算法求函数最大值python代码

遗传算法是一种常用的优化算法，它可以用来求解函数最大值问题。下面是一个简单的Python实现：

import random

# 遗传算法求函数最大值
# 目标函数：f(x) = x * sin(10 * pi * x) + 2.0
# 取值范围：-1 <= x <= 2

def fitness(x):
    return x * math.sin(10 * math.pi * x) + 2.0

def selection(population, fitness):
    # 选择算子，采用轮盘赌选择
    total_fitness = sum(fitness)
    r = random.uniform(0, total_fitness)
    for i, f in enumerate(fitness):
        r -= f
        if r <= 0:
            return population[i]

def crossover(parent1, parent2):
    # 交叉算子，采用单点交叉
    pos = random.randint(0, len(parent1) - 1)
    child1 = parent1[:pos] + parent2[pos:]
    child2 = parent2[:pos] + parent1[pos:]
    return child1, child2

def mutation(child, mutation_rate):
    # 变异算子，采用随机突变
    for i in range(len(child)):
        if random.random() < mutation_rate:
            child[i] = random.uniform(-1, 2)
    return child

# 参数设置
pop_size = 50 # 种群大小
gen_size = 100 # 迭代次数
mutation_rate = 0.01 # 变异概率

# 初始化种群
population = []
for i in range(pop_size):
    individual = [random.uniform(-1, 2) for j in range(10)]
    population.append(individual)

# 进化
for gen in range(gen_size):
    # 计算适应度
    fitness_list = [fitness(individual) for individual in population]
    
    # 选择
    new_population = []
    for i in range(pop_size):
        parent1 = selection(population, fitness_list)
        parent2 = selection(population, fitness_list)
        child1, child2 = crossover(parent1, parent2)
        child1 = mutation(child1, mutation_rate)
        child2 = mutation(child2, mutation_rate)
        new_population.append(child1)
        new_population.append(child2)
    
    population = new_population

# 找出最佳个体
best_individual = max(population, key=fitness)
best_fitness = fitness(best_individual)

print('best individual:', best_individual)
print('best fitness:', best_fitness)