遗传算法的自适应性研究:动态调整算法参数

    import random
    
    # 定义适应度函数
    def fitness_function(x):
    return x *
    
    # 定义遗传算法类
    class GeneticAlgorithm:
    def __init__(self, population_size, chromosome_length, mutation_rate, crossover_rate):
        self.population_size = population_size
        self.chromosome_length = chromosome_length
        self.mutation_rate = mutation_rate
        self.crossover_rate = crossover_rate
    
    # 初始化种群
    def initialize_population(self):
        population = []
        for i in range(self.population_size):
            chromosome = [random.randint(0, 1) for j in range(self.chromosome_length)]
            population.append(chromosome)
        return population
    
    # 计算适应度值
    def calculate_fitness(self, population):
        fitness_values = []
        for chromosome in population:
            x = int("".join(str(i) for i in chromosome), 2)
            fitness_values.append(fitness_function(x))
        return fitness_values
    
    # 选择操作
    def selection(self, population, fitness_values):
        # 使用轮盘赌选择
        total_fitness = sum(fitness_values)
        probabilities = [fitness / total_fitness for fitness in fitness_values]
        selected_indices = random.choices(range(len(population)), weights=probabilities, k=self.population_size)
        selected_population = [population[i] for i in selected_indices]
        return selected_population
    
    # 交叉操作
    def crossover(self, population):
        new_population = []
        for i in range(0, len(population), 2):
            parent1 = population[i]
            parent2 = population[i+1]
            if random.random() < self.crossover_rate:
                crossover_point = random.randint(1, self.chromosome_length - 1)
                child1 = parent1[:crossover_point] + parent2[crossover_point:]
                child2 = parent2[:crossover_point] + parent1[crossover_point:]
                new_population.extend([child1, child2])
            else:
                new_population.extend([parent1, parent2])
        return new_population
    
    # 变异操作
    def mutation(self, population):
        for i in range(len(population)):
            for j in range(self.chromosome_length):
                if random.random() < self.mutation_rate:
                    population[i][j] = 1 - population[i][j]
        return population
    
    # 运行遗传算法
    def run(self, iterations):
        population = self.initialize_population()
        for i in range(iterations):
            fitness_values = self.calculate_fitness(population)
            population = self.selection(population, fitness_values)
            population = self.crossover(population)
            population = self.mutation(population)
        best_chromosome = max(population, key=lambda chromosome: fitness_function(int("".join(str(i) for i in chromosome), 2)))
        best_fitness = fitness_function(int("".join(str(i) for i in best_chromosome), 2))
        return best_chromosome, best_fitness
    
    # 设置算法参数
    population_size = 100
    chromosome_length = 10
    mutation_rate = 0.01
    crossover_rate = 0.8
    
    # 创建遗传算法对象
    ga = GeneticAlgorithm(population_size, chromosome_length, mutation_rate, crossover_rate)
    
    # 运行遗传算法
    best_chromosome, best_fitness = ga.run(100)
    
    # 输出结果
    print("Best chromosome:", best_chromosome)
    print("Best fitness:", best_fitness)
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读