基于人工智能的排课系统源码设计与实现

import random
# 定义适应度函数
def fitness(chromosome):
conflicts = 0
for i in range(len(chromosome)):
for j in range(i + 1, len(chromosome)):
if chromosome[i] == chromosome[j]:
conflicts += 1
return 1 / (conflicts + 1)
# 初始化种群
def init_population(pop_size, chromosome_length):
population = []
for _ in range(pop_size):
chromosome = [random.randint(0, 4) for _ in range(chromosome_length)]
population.append(chromosome)
return population
# 遗传算法主函数
def genetic_algorithm(pop_size, chromosome_length, generations):
population = init_population(pop_size, chromosome_length)
for generation in range(generations):
population = sorted(population, key=fitness, reverse=True)
new_population = population[:2]
while len(new_population) < pop_size:
parent1, parent2 = random.choices(population[:5], k=2)
child = crossover(parent1, parent2)
mutate(child)
new_population.append(child)
population = new_population
return population[0]
# 交叉操作
def crossover(parent1, parent2):
point = random.randint(1, len(parent1) - 1)
child = parent1[:point] + parent2[point:]
return child
# 变异操作
def mutate(chromosome):
index = random.randint(0, len(chromosome) - 1)
chromosome[index] = random.randint(0, 4)
# 主程序入口
best_schedule = genetic_algorithm(pop_size=100, chromosome_length=7, generations=50)
print("Best schedule:", best_schedule)