{"title":"多目标问题的优化综合多项式变异算子类","authors":"Kent McClymont, E. Keedwell","doi":"10.1109/CEC.2010.5586076","DOIUrl":null,"url":null,"abstract":"This paper presents a novel method of generating new probability distributions tailored to specific problem classes for use in optimisation mutation operators. A range of tailored operators with varying behaviours are created using the proposed technique and the evolved multi-modal polynomial distributions are found to match the performance of a tuned Gaussian distribution when applied to a mutation operator incorporated in a simple (1+1) Evolution Strategy. The generated heuristics are shown to display a range of desirable characteristics for the DTLZ test problems 1, 2 and 7; such as speed of convergence.","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":"9 1","pages":"1-8"},"PeriodicalIF":0.0000,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Optimising multi-modal polynomial mutation operators for multi-objective problem classes\",\"authors\":\"Kent McClymont, E. Keedwell\",\"doi\":\"10.1109/CEC.2010.5586076\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a novel method of generating new probability distributions tailored to specific problem classes for use in optimisation mutation operators. A range of tailored operators with varying behaviours are created using the proposed technique and the evolved multi-modal polynomial distributions are found to match the performance of a tuned Gaussian distribution when applied to a mutation operator incorporated in a simple (1+1) Evolution Strategy. The generated heuristics are shown to display a range of desirable characteristics for the DTLZ test problems 1, 2 and 7; such as speed of convergence.\",\"PeriodicalId\":6344,\"journal\":{\"name\":\"2009 IEEE Congress on Evolutionary Computation\",\"volume\":\"9 1\",\"pages\":\"1-8\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-07-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 IEEE Congress on Evolutionary Computation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CEC.2010.5586076\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 IEEE Congress on Evolutionary Computation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CEC.2010.5586076","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Optimising multi-modal polynomial mutation operators for multi-objective problem classes
This paper presents a novel method of generating new probability distributions tailored to specific problem classes for use in optimisation mutation operators. A range of tailored operators with varying behaviours are created using the proposed technique and the evolved multi-modal polynomial distributions are found to match the performance of a tuned Gaussian distribution when applied to a mutation operator incorporated in a simple (1+1) Evolution Strategy. The generated heuristics are shown to display a range of desirable characteristics for the DTLZ test problems 1, 2 and 7; such as speed of convergence.