Efficient Identification of the Pareto Optimal Set
Ingrida Steponavice (),
Rob Hyndman (),
Kate Smith-Miles () and
Laura Villanova ()
No 12/14, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics
In this paper, we focus on expensive multiobjective optimization problems and propose a method to predict an approximation of the Pareto optimal set using classification of sampled decision vectors as dominated or nondominated. The performance of our method, called EPIC, is demonstrated on a set of benchmark problems used in the multiobjective optimization literature and compared with state-of the-art methods, ParEGO and PAL. The initial results are promising and encourage further research in this direction.
Keywords: multiobjective optimization; classification; expensive black-box function (search for similar items in EconPapers)
JEL-codes: C61 C90 C44 (search for similar items in EconPapers)
References: View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
http://business.monash.edu/econometrics-and-busine ... ions/ebs/wp12-14.pdf (application/pdf)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:msh:ebswps:2014-12
Ordering information: This working paper can be ordered from
http://business.mona ... -business-statistics
Access Statistics for this paper
More papers in Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics PO Box 11E, Monash University, Victoria 3800, Australia. Contact information at EDIRC.
Bibliographic data for series maintained by Dr Xibin Zhang ().