python遗传算法代码

以下是一个简单的Python遗传算法代码示例：

import random

# 定义目标函数，这里为了演示，使用一个简单的函数
def fitness_function(x):
    return x**2

# 生成初始种群
def generate_initial_population(population_size, chromosome_length):
    population = []
    for i in range(population_size):
        chromosome = []
        for j in range(chromosome_length):
            chromosome.append(random.randint(0, 1))
        population.append(chromosome)
    return population

# 计算适应度值
def calculate_fitness(population):
    fitness_values = []
    for chromosome in population:
        x = decode_chromosome(chromosome)
        fitness_values.append(fitness_function(x))
    return fitness_values

# 解码染色体
def decode_chromosome(chromosome):
    x = 0
    for i in range(len(chromosome)):
        x += chromosome[i] * 2**i
    return x

# 选择适应度高的个体
def selection(population, fitness_values):
    selected_population = []
    for i in range(len(population)):
        selected_population.append(random.choices(population, weights=fitness_values)[0])
    return selected_population

# 交叉操作
def crossover(parent1, parent2, crossover_rate):
    if random.random() < crossover_rate:
        crossover_point = random.randint(0, len(parent1)-1)
        child1 = parent1[:crossover_point] + parent2[crossover_point:]
        child2 = parent2[:crossover_point] + parent1[crossover_point:]
        return child1, child2
    else:
        return parent1, parent2

# 变异操作
def mutation(chromosome, mutation_rate):
    mutated_chromosome = []
    for gene in chromosome:
        if random.random() < mutation_rate:
            mutated_chromosome.append(1 - gene)
        else:
            mutated_chromosome.append(gene)
    return mutated_chromosome

# 主程序
population_size = 10
chromosome_length = 5
crossover_rate = 0.8
mutation_rate = 0.01
generations = 10

population = generate_initial_population(population_size, chromosome_length)
for i in range(generations):
    fitness_values = calculate_fitness(population)
    selected_population = selection(population, fitness_values)
    new_population = []
    for j in range(int(population_size/2)):
        parent1 = selected_population[j*2]
        parent2 = selected_population[j*2+1]
        child1, child2 = crossover(parent1, parent2, crossover_rate)
        child1 = mutation(child1, mutation_rate)
        child2 = mutation(child2, mutation_rate)
        new_population.append(child1)
        new_population.append(child2)
    population = new_population

# 输出最终结果
best_chromosome = max(population, key=lambda c: fitness_function(decode_chromosome(c)))
best_x = decode_chromosome(best_chromosome)
print("The best solution is x={}, f(x)={}".format(best_x, fitness_function(best_x)))

这个遗传算法代码实现的是一个求解 $f(x) = x^2$ 的最大值的问题。其中，generate_initial_population() 函数用来生成初始种群，calculate_fitness() 函数用来计算适应度值，selection() 函数用来进行选择操作，crossover() 函数用来进行交叉操作，mutation() 函数用来进行变异操作。最终，程序输出最优解。