量子遗传算法代码python

当然，下面是一个简单的量子遗传算法的 Python 代码示例：

import numpy as np

# 初始化种群
def initialize_population(pop_size, chromosome_length):
    population = np.random.randint(2, size=(pop_size, chromosome_length))
    return population

# 计算适应度
def fitness_function(chromosome):
    # 在这里计算适应度函数
    fitness = ...
    return fitness

# 选择操作
def selection(population, fitness):
    # 在这里进行选择操作，比如轮盘赌选择
    selected_indices = ...
    selected_population = population[selected_indices]
    return selected_population

# 交叉操作
def crossover(parent1, parent2):
    # 在这里进行交叉操作，比如单点交叉
    crossover_point = ...
    child1 = np.concatenate((parent1[:crossover_point], parent2[crossover_point:]))
    child2 = np.concatenate((parent2[:crossover_point], parent1[crossover_point:]))
    return child1, child2

# 变异操作
def mutation(chromosome, mutation_rate):
    # 在这里进行变异操作，比如随机翻转某些基因
    mutated_chromosome = chromosome.copy()
    for i in range(len(mutated_chromosome)):
        if np.random.random() < mutation_rate:
            mutated_chromosome[i] = 1 - mutated_chromosome[i]
    return mutated_chromosome

# 主函数
def quantum_genetic_algorithm(pop_size, chromosome_length, num_generations, mutation_rate):
    population = initialize_population(pop_size, chromosome_length)

    for generation in range(num_generations):
        fitness = np.array([fitness_function(chromosome) for chromosome in population])

        selected_population = selection(population, fitness)

        new_population = []
        while len(new_population) < pop_size:
            parent1, parent2 = np.random.choice(selected_population, size=2, replace=False)
            child1, child2 = crossover(parent1, parent2)
            mutated_child1 = mutation(child1, mutation_rate)
            mutated_child2 = mutation(child2, mutation_rate)
            new_population.extend([mutated_child1, mutated_child2])

        population = np.array(new_population)[:pop_size]

    best_chromosome = population[np.argmax(fitness)]
    best_fitness = fitness_function(best_chromosome)

    return best_chromosome, best_fitness

这只是一个简单的示例，你可以根据自己的需求进行修改和扩展。请注意，在代码中的 `fitness_function` 函数和其他操作中，你需要根据实际问题来定义适应度函数和相关操作。希望对你有帮助！