遗传算法的运用(python),给出其代码
时间: 2024-09-25 20:08:32 浏览: 3
遗传算法是一种搜索算法,受到自然选择和遗传学机制的启发,用于解决优化和搜索问题。在Python中应用遗传算法来解决旅行商问题(TSP),可以参考以下基本框架:
```python
import numpy as np
from random import sample, randint
# 定义适应度函数 - 对于TSP来说,就是路径长度的倒数,越长的路径适应度越低
def fitness(path, distance_matrix):
total_distance = sum(distance_matrix[path[i]][path[i + 1]] for i in range(len(path) - 1))
return 1 / total_distance
# 初始化种群
def init_population(pop_size, num_cities):
population = [sample(range(num_cities), num_cities) for _ in range(pop_size)]
return population
# 选择算子 - 轮盘赌选择
def selection(population, fitnesses, pop_size):
selected_pairs = []
for _ in range(pop_size // 2):
parent1 = roulette_wheel_selection(fitnesses)
parent2 = roulette_wheel_selection(fitnesses)
selected_pairs.append((parent1, parent2))
return selected_pairs
# 使用轮盘赌方式选出一个个体
def roulette_wheel_selection(fitnesses):
total_fitness = sum(fitnesses)
pick = randint(1, total_fitness)
current = 0
for index, individual_fitness in enumerate(fitnesses):
if current + individual_fitness >= pick:
return index
current += individual_fitness
# 交叉算子 - 部分匹配交叉
def crossover(parent1, parent2):
child1, child2 = [], []
start, end = sorted([randint(0, len(parent1)-1), randint(0, len(parent1)-1)])
# 复制片段
child1[start:end] = parent1[start:end]
child2[start:end] = parent2[start:end]
# 替换其余基因
for i in range(len(parent1)):
if not (start <= i < end):
remaining = list(set(child1) ^ set(parent2))
child1[i] = remaining.pop()
remaining = list(set(child2) ^ set(parent1))
child2[i] = remaining.pop()
return child1, child2
# 变异算子 - 交换两个位置上的值
def mutate(individual):
if random() < mutation_rate: # 以一定的概率发生变异
pos1, pos2 = randint(0, len(individual)-1), randint(0, len(individual)-1)
individual[pos1], individual[pos2] = individual[pos2], individual[pos1]
return individual
# 主循环
pop_size = 50
num_generations = 1000
mutation_rate = 0.02
population = init_population(pop_size, num_cities)
for generation in range(num_generations):
fitnesses = [fitness(ind, generate_random_distance_matrix(num_cities)) for ind in population]
mating_pool = selection(population, fitnesses, pop_size)
offspring = []
for parents in mating_pool:
child1, child2 = crossover(*parents)
offspring.extend([mutate(child1), mutate(child2)])
population = offspring
best_path = max(zip(fitnesses, population))[1]
print("Best path:", best_path)
```
以上代码仅为示例,并没有包括所有细节如距离矩阵的定义等,请根据具体情况进行调整。此脚本通过模拟自然进化过程中的选择、交叉与突变操作,在多代演化过程中逐步改善解决方案。注意实际使用时需要填充缺失部分比如`generate_random_distance_matrix`等函数的具体实现逻辑。