{"title":"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","authors":"Zhifeng Zhang, Junxia Ma, Xiao Cui","doi":"10.4018/IJGHPC.2021040104","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":43565,"journal":{"name":"International Journal of Grid and High Performance Computing","volume":"61 1","pages":"56-69"},"PeriodicalIF":0.6000,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Genetic Algorithm With Three-Dimensional Population Dominance Strategy for University Course Timetabling Problem\",\"authors\":\"Zhifeng Zhang, Junxia Ma, Xiao Cui\",\"doi\":\"10.4018/IJGHPC.2021040104\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":43565,\"journal\":{\"name\":\"International Journal of Grid and High Performance Computing\",\"volume\":\"61 1\",\"pages\":\"56-69\"},\"PeriodicalIF\":0.6000,\"publicationDate\":\"2021-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Grid and High Performance Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4018/IJGHPC.2021040104\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"COMPUTER SCIENCE, THEORY & METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Grid and High Performance Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/IJGHPC.2021040104","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
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.