Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/51608

TitleClustering-based selection for evolutionary many-objective optimization
Author(s)Denysiuk, Roman
Costa, Lino
Espírito Santo, I. A. C. P.
Issue date2014
PublisherSpringer
JournalLecture Notes in Computer Science
Abstract(s)This paper discusses a selection scheme allowing to employ a clustering technique to guide the search in evolutionary many-objective optimization. The underlying idea to avoid the curse of dimensionality is based on transforming the objective vectors before applying a clustering and the selection of cluster representatives according to the distance to a reference point. The experimental results reveal that the proposed approach is able to effectively guide the search in high-dimensional objective spaces, producing highly competitive performance when compared with state-of-the-art algorithms.
TypeConference paper
URIhttp://hdl.handle.net/1822/51608
ISBN978-3-319-10761-5
e-ISBN978-3-319-10762-2
DOI10.1007/978-3-319-10762-2_53
ISSN0302-9743
e-ISSN1611-3349
Peer-Reviewedyes
AccessRestricted access (UMinho)
Appears in Collections:CAlg - Artigos em livros de atas/Papers in proceedings

Files in This Item:
File Description SizeFormat 
c16.pdf
  Restricted access
150,51 kBAdobe PDFView/Open

Partilhe no FacebookPartilhe no TwitterPartilhe no DeliciousPartilhe no LinkedInPartilhe no DiggAdicionar ao Google BookmarksPartilhe no MySpacePartilhe no Orkut
Exporte no formato BibTex mendeley Exporte no formato Endnote Adicione ao seu ORCID