自适应遗传算法AGA代码python

以下是一个简单的自适应遗传算法(Adaptive Genetic Algorithm, AGA)的 Python 代码示例：

import random

# 遗传算法的参数设置
population_size = 50  # 种群大小
chromosome_length = 10  # 染色体长度
mutation_rate = 0.01  # 变异概率
crossover_rate = 0.8  # 交叉概率
max_generation = 100  # 最大迭代次数

# 初始化种群
def init_population():
    population = []
    for _ in range(population_size):
        chromosome = [random.randint(0, 1) for _ in range(chromosome_length)]
        population.append(chromosome)
    return population

# 计算个体适应度
def calculate_fitness(chromosome):
    fitness = sum(chromosome)
    return fitness

# 选择操作（轮盘赌选择）
def select(population):
    fitness_sum = sum(calculate_fitness(chromosome) for chromosome in population)
    probabilities = [calculate_fitness(chromosome) / fitness_sum for chromosome in population]
    cumulative_probabilities = [sum(probabilities[:i+1]) for i in range(len(probabilities))]
    
    selected_population = []
    for _ in range(population_size):
        rand = random.random()
        for i in range(len(cumulative_probabilities)):
            if rand <= cumulative_probabilities[i]:
                selected_population.append(population[i])
                break
    
    return selected_population

# 交叉操作（单点交叉）
def crossover(population):
    offspring_population = []
    for i in range(0, population_size, 2):
        parent1 = population[i]
        parent2 = population[i+1]
        
        if random.random() < crossover_rate:
            crossover_point = random.randint(0, chromosome_length - 1)
            offspring1 = parent1[:crossover_point] + parent2[crossover_point:]
            offspring2 = parent2[:crossover_point] + parent1[crossover_point:]
        else:
            offspring1 = parent1
            offspring2 = parent2
        
        offspring_population.extend([offspring1, offspring2])
    
    return offspring_population

# 变异操作（位翻转变异）
def mutate(population):
    for i in range(population_size):
        for j in range(chromosome_length):
            if random.random() < mutation_rate:
                population[i][j] = 1 - population[i][j]
    
    return population

# 主函数，执行遗传算法
def genetic_algorithm():
    population = init_population()
    generation = 0
    
    while generation < max_generation:
        selected_population = select(population)
        offspring_population = crossover(selected_population)
        mutated_population = mutate(offspring_population)
        
        population = mutated_population
        generation += 1
    
    best_individual = max(population, key=calculate_fitness)
    best_fitness = calculate_fitness(best_individual)
    
    print("Best individual:", best_individual)
    print("Best fitness:", best_fitness)

# 执行遗传算法
genetic_algorithm()

这段代码实现了一个简单的自适应遗传算法(Adaptive Genetic Algorithm, AGA)。其中，种群大小为 `population_size`，染色体长度为 `chromosome_length`，变异概率为 `mutation_rate`，交叉概率为 `crossover_rate`，最大迭代次数为 `max_generation`。代码中的遗传算法包括初始化种群、计算个体适应度、选择操作（轮盘赌选择）、交叉操作（单点交叉）和变异操作（位翻转变异）。最后输出迭代结束后的最佳个体和适应度。

请注意，这只是一个简单的示例代码，实际应用中可能需要根据具体问题进行修改和优化。