Efficient identification of the Pareto optimal set

Ingrida Steponavičė, Rob J Hyndman, Kate Smith-Miles, Laura Villanova
(2014) Learning and Intelligent Optimization, Lecture Notes in Computer Science, 8426, 341-352

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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.