Ingrida Steponavičė1, Rob J. Hyndman2, Kate Smith-Miles1 and Laura Villanova3
- School of Mathematical Sciences, Monash University, Clayton, Australia
- Department of Econometrics & Business Statistics, Monash University, Clayton, Australia
- Ceramic Fuel Cells Limited, Noble Park, Australia
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.