遗传算法python实例代码

下面是一个遗传算法的 Python 实例代码，我添加了一些注释来帮助你理解代码的每个部分。 ```python import random # 目标函数 def fitness(individual): # 计算个体的适应度，这里假设目标函数为求和 return sum(individual) # 初始化种群 def init_population(pop_size, gene_size): # 随机生成种群，每个个体包含 gene_size 个基因 population = [] for i in range(pop_size): individual = [random.randint(0, 1) for _ in range(gene_size)] population.append(individual) return population # 选择操作 def selection(population, num_parents): # 选择适应度最好的 num_parents 个个体作为父代 parents = [] for _ in range(num_parents): max_fitness = 0 max_individual = None for individual in population: if fitness(individual) > max_fitness: max_fitness = fitness(individual) max_individual = individual parents.append(max_individual) population.remove(max_individual) return parents # 交叉操作 def crossover(parents, offspring_size): # 随机选择两个父代进行交叉，生成 offspring_size 个后代 offspring = [] for _ in range(offspring_size): parent1 = random.choice(parents) parent2 = random.choice(parents) crossover_point = random.randint(1, len(parent1) - 1) offspring.append(parent1[:crossover_point] + parent2[crossover_point:]) return offspring # 变异操作 def mutation(offspring): # 对每个后代的每个基因进行变异，以一定概率将 0 变成 1，将 1 变成 0 for individual in offspring: for i in range(len(individual)): if random.random() < mutation_rate: individual[i] = 1 - individual[i] # 遗传算法主函数 def genetic_algorithm(pop_size, gene_size, num_parents, offspring_size, num_generations, mutation_rate): population = init_population(pop_size, gene_size) for i in range(num_generations): parents = selection(population, num_parents) offspring = crossover(parents, offspring_size) mutation(offspring) population = parents + offspring best_individual = max(population, key=fitness) return best_individual # 测试遗传算法函数 pop_size = 100 gene_size = 10 num_parents = 20 offspring_size = 80 num_generations = 100 mutation_rate = 0.1 best_individual = genetic_algorithm(pop_size, gene_size, num_parents, offspring_size, num_generations, mutation_rate) print("Best individual:", best_individual, "Fitness:", fitness(best_individual)) ``` 这是一个简单的遗传算法示例，包括以下函数： - `fitness()`：计算个体的适应度，这里假设目标函数为求和。 - `init_population()`：随机生成种群，每个个体包含 gene_size 个基因。 - `selection()`：选择适应度最好的 num_parents 个个体作为父代。 - `crossover()`：随机选择两个父代进行交叉，生成 offspring_size 个后代。 - `mutation()`：对每个后代的每个基因进行变异，以一定概率将 0 变成 1，将 1 变成 0。 - `genetic_algorithm()`：遗传算法主函数，在每一代中执行选择、交叉和变异操作，并返回最优个体。 在此示例中，我们使用遗传算法来优化一个包含 10 个基因的个体，目标函数为求和。算法将运行 100 代，种群大小为 100，每代选择适应度最好的 20 个个体作为父代，生成 80 个后代，并以 0.1 的概率对每个后代的每个基因进行变异。最终输出最优个体和其适应度。

以下是一个遗传算法的 Python 实现示例： ```python import random # 定义目标函数（适应度函数） def fitness_function(x): return x**2 # 定义遗传算法相关参数 population_size = 100 chromosome_length = 5 mutation_rate = 0.01 crossover_rate = 0.6 generations = 50 # 初始化种群 def init_population(): 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 # 计算种群中每个染色体的适应度 def calculate_fitness(population): fitness_scores = [] for chromosome in population: x = int(''.join(map(str, chromosome)), 2) fitness_scores.append(fitness_function(x)) return fitness_scores # 选择操作：轮盘赌选择 def selection(population, fitness_scores): total_fitness = sum(fitness_scores) probabilities = [fitness_score/total_fitness for fitness_score in fitness_scores] selected_population = [] for i in range(population_size): selected_chromosome = None while selected_chromosome is None: for j in range(population_size): if random.random() <= probabilities[j]: selected_chromosome = population[j] break selected_population.append(selected_chromosome) return selected_population # 交叉操作：单点交叉 def crossover(population): new_population = [] for i in range(population_size): parent1 = population[random.randint(0, population_size-1)] parent2 = population[random.randint(0, population_size-1)] if random.random() <= crossover_rate: crossover_point = random.randint(0, chromosome_length-1) child1 = parent1[:crossover_point] + parent2[crossover_point:] child2 = parent2[:crossover_point] + parent1[crossover_point:] new_population.append(child1) new_population.append(child2) else: new_population.append(parent1) new_population.append(parent2) return new_population # 变异操作：单点变异 def mutation(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 # 运行遗传算法 population = init_population() for i in range(generations): fitness_scores = calculate_fitness(population) print('Generation:', i+1, ', Best Fitness:', max(fitness_scores)) selected_population = selection(population, fitness_scores) new_population = crossover(selected_population) mutated_population = mutation(new_population) population = mutated_population # 输出最终结果 best_chromosome = None best_fitness = 0 for chromosome in population: x = int(''.join(map(str, chromosome)), 2) fitness = fitness_function(x) if fitness > best_fitness: best_chromosome = chromosome best_fitness = fitness print('Best Chromosome:', best_chromosome) print('Best Fitness:', best_fitness) ``` 该代码实现了一个简单的遗传算法，用于寻找一个函数的最大值。在这个例子中，我们定义了一个目标函数 `fitness_function(x)`，并将其作为适应度函数。我们使用二进制串表示染色体，并使用轮盘赌选择、单点交叉和单点变异三种遗传算法操作对种群进行进化。最终输出找到的最佳染色体和适应度。