Genetic Algorithm遗传算法学习

好的，我了解您的问题了。以下是一个简单的Python实现遗传算法的示例代码： ```python import random # 定义目标函数 def fitness_function(x): return x**2 # 定义种群数量 POPULATION_SIZE = 100 # 定义染色体长度 CHROMOSOME_LENGTH = 20 # 定义交叉概率 CROSSOVER_RATE = 0.8 # 定义变异概率 MUTATION_RATE = 0.1 # 定义种群 population = [] # 初始化种群 for i in range(POPULATION_SIZE): chromosome = [] for j in range(CHROMOSOME_LENGTH): chromosome.append(random.randint(0, 1)) population.append(chromosome) # 迭代次数 GENERATIONS = 500 # 开始迭代 for generation in range(GENERATIONS): # 计算适应度 fitness_values = [] for chromosome in population: x = int(''.join(map(str, chromosome)), 2) fitness_values.append(fitness_function(x)) # 找到最优解 best_fitness = max(fitness_values) best_chromosome = population[fitness_values.index(best_fitness)] # 输出结果 print("Generation:", generation, "Best fitness:", best_fitness, "Best chromosome:", best_chromosome) # 选择 parents = [] for i in range(POPULATION_SIZE): parent1 = population[random.randint(0, POPULATION_SIZE-1)] parent2 = population[random.randint(0, POPULATION_SIZE-1)] if fitness_function(int(''.join(map(str, parent1)), 2)) > fitness_function(int(''.join(map(str, parent2)), 2)): parents.append(parent1) else: parents.append(parent2) # 交叉 offspring = [] for i in range(0, POPULATION_SIZE, 2): parent1 = parents[i] parent2 = parents[i+1] if random.random() < CROSSOVER_RATE: crossover_point = random.randint(1, CHROMOSOME_LENGTH-1) offspring1 = parent1[:crossover_point] + parent2[crossover_point:] offspring2 = parent2[:crossover_point] + parent1[crossover_point:] offspring.append(offspring1) offspring.append(offspring2) else: offspring.append(parent1) offspring.append(parent2) # 变异 for i in range(POPULATION_SIZE): chromosome = offspring[i] for j in range(CHROMOSOME_LENGTH): if random.random() < MUTATION_RATE: chromosome[j] = 1 - chromosome[j] # 更新种群 population = offspring ``` 这个示例实现了一个简单的遗传算法来求解目标函数 $f(x)=x^2$ 的最大值，其中 $x$ 是一个20位的二进制数。在每个迭代中，选择、交叉和变异操作被执行以生成新的种群。最终找到最优解并输出结果。 请注意，这只是一个简单的示例，实际应用中需要根据具体问题进行适当的修改和调整。