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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

Abstract: 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)
Date: 2014
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