用遗传算法对目标函数寻优的Python代码

当使用遗传算法来对目标函数进行优化时，您可以参考以下的Python代码示例：

import random
import numpy as np

# 目标函数（示例：要寻找的最小值）
def objective_function(x):
    return x**2

# 初始化种群
def initialize_population(population_size, chromosome_length, lower_bound, upper_bound):
    population = []
    for _ in range(population_size):
        chromosome = [random.uniform(lower_bound, upper_bound) for _ in range(chromosome_length)]
        population.append(chromosome)
    return population

# 计算适应度值
def calculate_fitness(population):
    fitness_values = []
    for chromosome in population:
        fitness = objective_function(chromosome[0])
        fitness_values.append(fitness)
    return fitness_values

# 选择操作（轮盘赌选择）
def selection(population, fitness_values):
    probabilities = np.array(fitness_values) / sum(fitness_values)
    selected_population = random.choices(population, probabilities, k=len(population))
    return selected_population

# 交叉操作（单点交叉）
def crossover(parent1, parent2):
    crossover_point = random.randint(0, len(parent1) - 1)
    offspring = parent1[:crossover_point] + parent2[crossover_point:]
    return offspring

# 变异操作（高斯变异）
def mutation(individual, mutation_rate, lower_bound, upper_bound):
    mutated_individual = []
    for gene in individual:
        if random.random() < mutation_rate:
            mutated_gene = gene + random.gauss(0, 1)
            mutated_gene = max(min(mutated_gene, upper_bound), lower_bound)
            mutated_individual.append(mutated_gene)
        else:
            mutated_individual.append(gene)
    return mutated_individual

# 遗传优化算法主函数
def genetic_algorithm(population_size, chromosome_length, lower_bound, upper_bound, generations, mutation_rate):
    population = initialize_population(population_size, chromosome_length, lower_bound, upper_bound)

    for _ in range(generations):
        fitness_values = calculate_fitness(population)
        selected_population = selection(population, fitness_values)

        new_population = []
        while len(new_population) < population_size:
            parent1 = random.choice(selected_population)
            parent2 = random.choice(selected_population)
            offspring = crossover(parent1, parent2)
            offspring = mutation(offspring, mutation_rate, lower_bound, upper_bound)
            new_population.append(offspring)

        population = new_population

    best_individual = min(population, key=lambda x: objective_function(x[0]))
    return best_individual

# 示例用法
population_size = 100
chromosome_length = 1
lower_bound = -10
upper_bound = 10
generations = 50
mutation_rate = 0.1

best_individual = genetic_algorithm(population_size, chromosome_length, lower_bound, upper_bound, generations, mutation_rate)
print("Best Individual:", best_individual)
print("Best Fitness:", objective_function(best_individual[0]))

在这个示例代码中，我们假设目标函数是一个简单的函数 `x^2`。您可以根据实际情况修改目标函数的定义。遗传算法的其他操作，如选择操作、交叉操作和变异操作，也可以根据需求进行修改和扩展。

请注意，这个示例代码中的遗传算法是针对单变量优化问题的。如果您需要解决多变量优化问题，可以相应地修改染色体的长度和适应度计算方法。

希望这个示例对您有帮助！