Efficient Identification of the Pareto Optimal Set

  • Ingrida Steponavičė
  • Rob J. Hyndman
  • Kate Smith-Miles
  • Laura Villanova
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8426)


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.


Multiobjective optimization Classification Expensive black-box function 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Ingrida Steponavičė
    • 1
  • Rob J. Hyndman
    • 2
  • Kate Smith-Miles
    • 1
  • Laura Villanova
    • 3
  1. 1.School of Mathematical SciencesMonash UniversityClaytonAustralia
  2. 2.Department of Econometrics and Business StatisticsMonash UniversityClaytonAustralia
  3. 3.Ceramic Fuel Cells LimitedNoble ParkAustralia

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