Efficient identification of the Pareto optimal set

 

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.

Cite

@inproceedings{epic,
  author    = {Ingrida Steponavičė and Rob J Hyndman and Kate Smith-Miles and Laura Villanova},
  booktitle = {Learning and Intelligent Optimization: Lecture Notes in Computer Science},
  doi       = {10.1007/978-3-319-09584-4_29},
  pages     = {341-352},
  publisher = {Springer},
  title     = {Efficient identification of the {Pareto} optimal set},
  volume    = {8426},
  year      = 2014
}