{"title":"即插即用超启发式:一个扩展公式","authors":"Patricia Ryser-Welch, J. Miller","doi":"10.1109/SASO.2014.33","DOIUrl":null,"url":null,"abstract":"Hyper-heuristics is a very active field that is developing all the time. This area of bio-inspired intelligent systems covers a wide range of algorithms selection techniques. This type of self-organising mechanism uses heuristics to optimise heuristics. Many discussions focus on the quality of solutions of the problems obtained from the hyper-heuristics, very little discussion concentrates on the generated algorithms themselves. Some hyper-heuristic frameworks tend to be highly constrained, their limited instruction sets prevent the state-of-the-art algorithms from being expressed. In addition, often the generated algorithms are not human-readable. In this paper, we propose a possible extension of some existing hyper-heuristic formulations, so that some of the current open issues can be addressed and it becomes possible to produce self-organizing heuristics that adapt themselves automatically to the environment when the class of problems changes. This together with the analysis of the evolved algorithms, may lead to human-competitive results.","PeriodicalId":6458,"journal":{"name":"2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems Workshops","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2014-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Plug-and-Play Hyper-heuristics: An Extended Formulation\",\"authors\":\"Patricia Ryser-Welch, J. Miller\",\"doi\":\"10.1109/SASO.2014.33\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Hyper-heuristics is a very active field that is developing all the time. This area of bio-inspired intelligent systems covers a wide range of algorithms selection techniques. This type of self-organising mechanism uses heuristics to optimise heuristics. Many discussions focus on the quality of solutions of the problems obtained from the hyper-heuristics, very little discussion concentrates on the generated algorithms themselves. Some hyper-heuristic frameworks tend to be highly constrained, their limited instruction sets prevent the state-of-the-art algorithms from being expressed. In addition, often the generated algorithms are not human-readable. In this paper, we propose a possible extension of some existing hyper-heuristic formulations, so that some of the current open issues can be addressed and it becomes possible to produce self-organizing heuristics that adapt themselves automatically to the environment when the class of problems changes. This together with the analysis of the evolved algorithms, may lead to human-competitive results.\",\"PeriodicalId\":6458,\"journal\":{\"name\":\"2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems Workshops\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-09-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems Workshops\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SASO.2014.33\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems Workshops","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SASO.2014.33","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Plug-and-Play Hyper-heuristics: An Extended Formulation
Hyper-heuristics is a very active field that is developing all the time. This area of bio-inspired intelligent systems covers a wide range of algorithms selection techniques. This type of self-organising mechanism uses heuristics to optimise heuristics. Many discussions focus on the quality of solutions of the problems obtained from the hyper-heuristics, very little discussion concentrates on the generated algorithms themselves. Some hyper-heuristic frameworks tend to be highly constrained, their limited instruction sets prevent the state-of-the-art algorithms from being expressed. In addition, often the generated algorithms are not human-readable. In this paper, we propose a possible extension of some existing hyper-heuristic formulations, so that some of the current open issues can be addressed and it becomes possible to produce self-organizing heuristics that adapt themselves automatically to the environment when the class of problems changes. This together with the analysis of the evolved algorithms, may lead to human-competitive results.