利用超启发式进化算法解决护士名册问题的初步研究

Christopher Rae, N. Pillay
{"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}
引用次数: 3

摘要

本文报道了用进化算法选择微扰超启发式方法解决护士名册问题的初步尝试。本研究的主要目的是得到一个感觉的潜力,这种超启发式在解决护士名册问题。这将用于指导这项工作的未来扩展。本研究确定了该领域的低级微扰启发式,以及进化算法超启发式的表示、初始种群生成方法、评估和选择方法以及遗传算子。该方法在第一届国际护士名册竞赛的六个问题上进行了测试。发现超启发式的性能与应用于相同问题的其他方法相当。这项研究显示了这种方法的潜力,并确定了这项工作的未来扩展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信