Harold D. De Mello, A. V. Abs da Cruz, M. Vellasco
{"title":"Estimation of Distribution Algorithm Based on a Multivariate Extension of the Archimedean Copula","authors":"Harold D. De Mello, A. V. Abs da Cruz, M. Vellasco","doi":"10.1109/BRICS-CCI-CBIC.2013.23","DOIUrl":null,"url":null,"abstract":"This paper presents a Copula-based Estimation of Distribution Algorithm with Parameter Updating for numeric optimization problems. This model implements an estimation of distribution algorithm using a multivariate extension of the Archimedean copula (MEC-EDA) to estimate the conditional probability for generating a population of individuals. Moreover, the model uses traditional crossover and elitism operators during the optimization. We show that this approach improves the overall performance of the optimization when compared to other copula-based EDAs.","PeriodicalId":306195,"journal":{"name":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BRICS-CCI-CBIC.2013.23","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
This paper presents a Copula-based Estimation of Distribution Algorithm with Parameter Updating for numeric optimization problems. This model implements an estimation of distribution algorithm using a multivariate extension of the Archimedean copula (MEC-EDA) to estimate the conditional probability for generating a population of individuals. Moreover, the model uses traditional crossover and elitism operators during the optimization. We show that this approach improves the overall performance of the optimization when compared to other copula-based EDAs.