{"title":"并行进化算法中的变大小种群","authors":"Gabriela F. Minetti, Hugo Alfonso","doi":"10.1109/ISDA.2005.99","DOIUrl":null,"url":null,"abstract":"Considering the population size is a critical parameter to define in evolutionary computation, in this paper an improved parallel evolutionary algorithm that incorporates different mechanisms to adapt the population size to the current status, is presented. Those mechanisms are based on resizing on fitness improvement GA (PRoFIGA) and variable population size (GAVaPS). Results indicate these incorporations are a reasonable choice when refinement in solutions is necessary.","PeriodicalId":345842,"journal":{"name":"5th International Conference on Intelligent Systems Design and Applications (ISDA'05)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2005-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"Variable size population in parallel evolutionary algorithms\",\"authors\":\"Gabriela F. Minetti, Hugo Alfonso\",\"doi\":\"10.1109/ISDA.2005.99\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Considering the population size is a critical parameter to define in evolutionary computation, in this paper an improved parallel evolutionary algorithm that incorporates different mechanisms to adapt the population size to the current status, is presented. Those mechanisms are based on resizing on fitness improvement GA (PRoFIGA) and variable population size (GAVaPS). Results indicate these incorporations are a reasonable choice when refinement in solutions is necessary.\",\"PeriodicalId\":345842,\"journal\":{\"name\":\"5th International Conference on Intelligent Systems Design and Applications (ISDA'05)\",\"volume\":\"108 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2005-09-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"5th International Conference on Intelligent Systems Design and Applications (ISDA'05)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISDA.2005.99\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"5th International Conference on Intelligent Systems Design and Applications (ISDA'05)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISDA.2005.99","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Variable size population in parallel evolutionary algorithms
Considering the population size is a critical parameter to define in evolutionary computation, in this paper an improved parallel evolutionary algorithm that incorporates different mechanisms to adapt the population size to the current status, is presented. Those mechanisms are based on resizing on fitness improvement GA (PRoFIGA) and variable population size (GAVaPS). Results indicate these incorporations are a reasonable choice when refinement in solutions is necessary.
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