{"title":"具有自适应突变率的标记遗传算法","authors":"P. Hartono, S. Hashimoto, M. Wahde","doi":"10.1109/CEC.2004.1331121","DOIUrl":null,"url":null,"abstract":"In This work we propose a modified GA that assigns a unique mutation rate to each gene based on the contribution of the respective gene's contribution to the fitness of the individual. Although the proposed model is not \"parameter free\", through a number of experiments, we show that the parameters for this model are significantly insensitive to the landscape of the problems compared with the mutation rate in conventional GA, implying that this model could deal effectively with a wide range of problems the requirement to set the mutation rate empirically.","PeriodicalId":152088,"journal":{"name":"Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2004-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Labeled-GA with adaptive mutation rate\",\"authors\":\"P. Hartono, S. Hashimoto, M. Wahde\",\"doi\":\"10.1109/CEC.2004.1331121\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In This work we propose a modified GA that assigns a unique mutation rate to each gene based on the contribution of the respective gene's contribution to the fitness of the individual. Although the proposed model is not \\\"parameter free\\\", through a number of experiments, we show that the parameters for this model are significantly insensitive to the landscape of the problems compared with the mutation rate in conventional GA, implying that this model could deal effectively with a wide range of problems the requirement to set the mutation rate empirically.\",\"PeriodicalId\":152088,\"journal\":{\"name\":\"Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753)\",\"volume\":\"21 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2004-06-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CEC.2004.1331121\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CEC.2004.1331121","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In This work we propose a modified GA that assigns a unique mutation rate to each gene based on the contribution of the respective gene's contribution to the fitness of the individual. Although the proposed model is not "parameter free", through a number of experiments, we show that the parameters for this model are significantly insensitive to the landscape of the problems compared with the mutation rate in conventional GA, implying that this model could deal effectively with a wide range of problems the requirement to set the mutation rate empirically.