{"title":"并行遗传算法中种群多样性的理论研究","authors":"Mei-Qin Pan, Guo-ping He","doi":"10.1109/ICMLC.2002.1176799","DOIUrl":null,"url":null,"abstract":"In this paper, conditional probability density and marginal distribution are proposed as measures of population in genetic algorithms. The influence of selection, crossover and mutation on population distribution is analyzed. In addition, the recursive equations governing population density are derived, and a conclusion of global convergence is also shown.","PeriodicalId":90702,"journal":{"name":"Proceedings. International Conference on Machine Learning and Cybernetics","volume":"2014 1","pages":"472-475 vol.1"},"PeriodicalIF":0.0000,"publicationDate":"2002-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Theoretical study on diversity of population in parallel genetic algorithms\",\"authors\":\"Mei-Qin Pan, Guo-ping He\",\"doi\":\"10.1109/ICMLC.2002.1176799\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, conditional probability density and marginal distribution are proposed as measures of population in genetic algorithms. The influence of selection, crossover and mutation on population distribution is analyzed. In addition, the recursive equations governing population density are derived, and a conclusion of global convergence is also shown.\",\"PeriodicalId\":90702,\"journal\":{\"name\":\"Proceedings. International Conference on Machine Learning and Cybernetics\",\"volume\":\"2014 1\",\"pages\":\"472-475 vol.1\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2002-11-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings. International Conference on Machine Learning and Cybernetics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICMLC.2002.1176799\",\"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. International Conference on Machine Learning and Cybernetics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMLC.2002.1176799","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Theoretical study on diversity of population in parallel genetic algorithms
In this paper, conditional probability density and marginal distribution are proposed as measures of population in genetic algorithms. The influence of selection, crossover and mutation on population distribution is analyzed. In addition, the recursive equations governing population density are derived, and a conclusion of global convergence is also shown.
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