{"title":"求解大学排课问题的并行化遗传算法","authors":"K. Banczyk, Tomasz Boinski, H. Krawczyk","doi":"10.1109/PARELEC.2006.64","DOIUrl":null,"url":null,"abstract":"Genetic algorithms play an important role in solving many optimisation problems. The paper concentrates on the design of a parallel genetic algorithm for obtaining acceptable and possibly good university timetables. Some known parallelisation techniques are introduced and the chosen implementation using MPI platform is shown. The master-slave management structure is assumed and the system scalability and the solution quality as function of the processing node number and population size are estimated","PeriodicalId":186915,"journal":{"name":"International Conference on Parallel Computing in Electrical Engineering","volume":"56 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Parallelisation of Genetic Algorithms for Solving University Timetabling Problems\",\"authors\":\"K. Banczyk, Tomasz Boinski, H. Krawczyk\",\"doi\":\"10.1109/PARELEC.2006.64\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Genetic algorithms play an important role in solving many optimisation problems. The paper concentrates on the design of a parallel genetic algorithm for obtaining acceptable and possibly good university timetables. Some known parallelisation techniques are introduced and the chosen implementation using MPI platform is shown. The master-slave management structure is assumed and the system scalability and the solution quality as function of the processing node number and population size are estimated\",\"PeriodicalId\":186915,\"journal\":{\"name\":\"International Conference on Parallel Computing in Electrical Engineering\",\"volume\":\"56 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Conference on Parallel Computing in Electrical Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/PARELEC.2006.64\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Conference on Parallel Computing in Electrical Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PARELEC.2006.64","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Parallelisation of Genetic Algorithms for Solving University Timetabling Problems
Genetic algorithms play an important role in solving many optimisation problems. The paper concentrates on the design of a parallel genetic algorithm for obtaining acceptable and possibly good university timetables. Some known parallelisation techniques are introduced and the chosen implementation using MPI platform is shown. The master-slave management structure is assumed and the system scalability and the solution quality as function of the processing node number and population size are estimated
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