基于三维种群优势策略的遗传算法求解大学课程排课问题

IF 0.6 Q4 COMPUTER SCIENCE, THEORY & METHODS International Journal of Grid and High Performance Computing Pub Date : 2021-04-01 DOI:10.4018/IJGHPC.2021040104
Zhifeng Zhang, Junxia Ma, Xiao Cui
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引用次数: 1

摘要

近年来,随着招生规模的不断扩大和教学改革的深入,如何利用有限的教师资源和有限的课堂资源,制定合理的大学课程时间表受到了人们的极大关注。本文首先对高校课程排课问题进行了研究,提出了相应的数学模型,并构建了相应的求解框架。随后,针对大学课程排课问题的特点,将遗传算法引入到大学课程排课问题的求解中,并提出了三维编码策略、适应度函数设计策略、初始种群生成策略、种群优势策略、自适应交叉概率策略等改进策略。并采用自适应突变概率策略对遗传算法进行优化。仿真结果表明,所提出的遗传算法能够有效地解决高校课程排课问题。
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Genetic Algorithm With Three-Dimensional Population Dominance Strategy for University Course Timetabling Problem
In recent years, with the growing expansion of the recruitment scale and the further reform in teaching, how to use the limited teacher resources and the limited classroom resources to schedule a reasonable university course timetable has gotten great interest. In this paper, the authors firstly hashed over the university course timetabling problem, and then they presented the related mathematical model and constructed the relevant solution framework. Subsequently, in view of characteristics of the university course timetabling problem, they introduced genetic algorithm to solve the university course timetabling problem and proposed many improvement strategies which include the three-dimensional coding strategy, the fitness function design strategy, the initial population generation strategy, the population dominance strategy, the adaptive crossover probability strategy, and the adaptive mutation probability strategy to optimize genetic algorithm. Simulation results show that the proposed genetic algorithm can solve the university course timetabling problem effectively.
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来源期刊
CiteScore
1.70
自引率
10.00%
发文量
24
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