{"title":"利用超启发式进化算法解决护士名册问题的初步研究","authors":"Christopher Rae, N. Pillay","doi":"10.1109/NaBIC.2012.6402255","DOIUrl":null,"url":null,"abstract":"This paper reports on an initial attempt to solve the nurse rostering problem using an evolutionary algorithm selection perturbative hyper-heuristic. The main aim of this study is to get a feel for the potential of such a hyper-heuristic in solving the nurse rostering problem. This will be used to direct future extensions of this work. This study identifies low-level perturbative heuristics for this domain as well as a representation, initial population generation method, evaluation and selection methods, and genetic operator for the evolutionary algorithm hyper-heuristic. The approach was tested on six problems from the first international nurse rostering competition. The performance of the hyper-heuristic was found to be comparable to that of other methods applied to the same problems. The study has shown the potential of this approach and also identified future extensions of this work.","PeriodicalId":103091,"journal":{"name":"2012 Fourth World Congress on Nature and Biologically Inspired Computing (NaBIC)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"A preliminary study into the use of an evolutionary algorithm hyper-heuristic to solve the nurse rostering problem\",\"authors\":\"Christopher Rae, N. Pillay\",\"doi\":\"10.1109/NaBIC.2012.6402255\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper reports on an initial attempt to solve the nurse rostering problem using an evolutionary algorithm selection perturbative hyper-heuristic. The main aim of this study is to get a feel for the potential of such a hyper-heuristic in solving the nurse rostering problem. This will be used to direct future extensions of this work. This study identifies low-level perturbative heuristics for this domain as well as a representation, initial population generation method, evaluation and selection methods, and genetic operator for the evolutionary algorithm hyper-heuristic. The approach was tested on six problems from the first international nurse rostering competition. The performance of the hyper-heuristic was found to be comparable to that of other methods applied to the same problems. The study has shown the potential of this approach and also identified future extensions of this work.\",\"PeriodicalId\":103091,\"journal\":{\"name\":\"2012 Fourth World Congress on Nature and Biologically Inspired Computing (NaBIC)\",\"volume\":\"39 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 Fourth World Congress on Nature and Biologically Inspired Computing (NaBIC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NaBIC.2012.6402255\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 Fourth World Congress on Nature and Biologically Inspired Computing (NaBIC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NaBIC.2012.6402255","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A preliminary study into the use of an evolutionary algorithm hyper-heuristic to solve the nurse rostering problem
This paper reports on an initial attempt to solve the nurse rostering problem using an evolutionary algorithm selection perturbative hyper-heuristic. The main aim of this study is to get a feel for the potential of such a hyper-heuristic in solving the nurse rostering problem. This will be used to direct future extensions of this work. This study identifies low-level perturbative heuristics for this domain as well as a representation, initial population generation method, evaluation and selection methods, and genetic operator for the evolutionary algorithm hyper-heuristic. The approach was tested on six problems from the first international nurse rostering competition. The performance of the hyper-heuristic was found to be comparable to that of other methods applied to the same problems. The study has shown the potential of this approach and also identified future extensions of this work.