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Algorithm Portfolios for Noisy Optimization: Compare Solvers Early Read chapter Read first chapter

2014 | OriginalPaper | Chapter

Efficient Identification of the Pareto Optimal Set

Authors : Ingrida Steponavičė, Rob J. Hyndman, Kate Smith-Miles, Laura Villanova

Published in: Learning and Intelligent Optimization

Publisher: Springer International Publishing

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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.

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Metadata
Title
Efficient Identification of the Pareto Optimal Set
Authors
Ingrida Steponavičė
Rob J. Hyndman
Kate Smith-Miles
Laura Villanova
Copyright Year
2014
Book
Learning and Intelligent Optimization

Print ISBN: 978-3-319-09583-7

Electronic ISBN: 978-3-319-09584-4

Copyright Year: 2014

DOI
https://doi.org/10.1007/978-3-319-09584-4_29

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