{"title":"Adapting mutations in genetic algorithms using gene flow principles","authors":"G. Greenwood","doi":"10.1109/CEC.2003.1299833","DOIUrl":null,"url":null,"abstract":"Bit mutation in genetic algorithms is usually done with a single fixed probability. Methods to adapt this probability have been suggested, but they operate at the genome level. This paper describes a gene level adaption scheme, based on allele frequencies, which gives a better escape from local optima.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2003-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CEC.2003.1299833","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
Bit mutation in genetic algorithms is usually done with a single fixed probability. Methods to adapt this probability have been suggested, but they operate at the genome level. This paper describes a gene level adaption scheme, based on allele frequencies, which gives a better escape from local optima.
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