{"title":"有限总体模型下分布估计算法的分析","authors":"Yan Wu, Yuping Wang, Xiaoxiong Liu","doi":"10.1109/ICNC.2007.174","DOIUrl":null,"url":null,"abstract":"The convergence of estimation of distribution algorithms (EDAs) with finite population is analyzed in this paper. At first, the models of EDAs with finite population are designed by incorporating an error into expected distribution of parent population. Then the convergence of the EDAs is proved with finite population under three widely used selection schemes. The results show that EDAs converge to the optimal solutions within the range of error described in this paper.","PeriodicalId":250881,"journal":{"name":"Third International Conference on Natural Computation (ICNC 2007)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"An Analysis of Estimation of Distribution Algorithms with Finite Population Models\",\"authors\":\"Yan Wu, Yuping Wang, Xiaoxiong Liu\",\"doi\":\"10.1109/ICNC.2007.174\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The convergence of estimation of distribution algorithms (EDAs) with finite population is analyzed in this paper. At first, the models of EDAs with finite population are designed by incorporating an error into expected distribution of parent population. Then the convergence of the EDAs is proved with finite population under three widely used selection schemes. The results show that EDAs converge to the optimal solutions within the range of error described in this paper.\",\"PeriodicalId\":250881,\"journal\":{\"name\":\"Third International Conference on Natural Computation (ICNC 2007)\",\"volume\":\"19 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-08-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Third International Conference on Natural Computation (ICNC 2007)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICNC.2007.174\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Third International Conference on Natural Computation (ICNC 2007)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICNC.2007.174","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An Analysis of Estimation of Distribution Algorithms with Finite Population Models
The convergence of estimation of distribution algorithms (EDAs) with finite population is analyzed in this paper. At first, the models of EDAs with finite population are designed by incorporating an error into expected distribution of parent population. Then the convergence of the EDAs is proved with finite population under three widely used selection schemes. The results show that EDAs converge to the optimal solutions within the range of error described in this paper.
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