dc.creatorFerreira
dc.creatorWallace G.; Serpa
dc.creatorAlberto L.
dc.date2016
dc.datemaio
dc.date2017-11-13T13:23:10Z
dc.date2017-11-13T13:23:10Z
dc.date.accessioned2018-03-29T05:55:52Z
dc.date.available2018-03-29T05:55:52Z
dc.identifierStructural And Multidisciplinary Optimization. Springer, v. 53, p. 1019 - 1046, 2016.
dc.identifier1615-147X
dc.identifier1615-1488
dc.identifierWOS:000374972500008
dc.identifier10.1007/s00158-015-1366-1
dc.identifierhttps://link-springer-com.ez88.periodicos.capes.gov.br/article/10.1007%2Fs00158-015-1366-1
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/328046
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1365071
dc.descriptionIn this work we present an approach to create ensemble of metamodels (or weighted averaged surrogates) based on least squares (LS) approximation. The LS approach is appealing since it is possible to estimate the ensemble weights without using any explicit error metrics as in most of the existent ensemble methods. As an additional feature, the LS based ensemble of metamodels has a prediction variance function that enables the extension to the efficient global optimization. The proposed LS approach is a variation of the standard LS regression by augmenting the matrices in such a way that minimizes the effects of multicollinearity inherent to calculation of the ensemble weights. We tested and compared the augmented LS approach with different LS variants and also with existent ensemble methods, by means of analytical and real-world functions from two to forty-four variables. The augmented least squares approach performed with good accuracy and stability for prediction purposes, in the same level of other ensemble methods and has computational cost comparable to the faster ones.
dc.description53
dc.description5
dc.description1019
dc.description1046
dc.languageEnglish
dc.publisherSpringer
dc.publisherNew York
dc.relationStructural and Multidisciplinary Optimization
dc.rightsfechado
dc.sourceWOS
dc.subjectEnsemble Of Metamodels
dc.subjectWeighted Average Surrogates
dc.subjectLeast Squares Approximation
dc.titleEnsemble Of Metamodels: The Augmented Least Squares Approach
dc.typeArtículos de revistas


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