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Adaptive sampling of Pareto frontiers with binary constraints using regression and classification

: Heese, R.; Bortz, M.


Institute of Electrical and Electronics Engineers -IEEE-; IEEE Computer Society:
ICPR 2020, 25th International Conference on Pattern Recognition. Proceedings : 10-15 January 2021, Milan, Italy, Virtual
Piscataway, NJ: IEEE, 2021
ISBN: 978-1-7281-8809-6
ISBN: 978-1-7281-8808-9
International Conference on Pattern Recognition (ICPR) <25, 2021, Online>
Conference Paper
Fraunhofer ITWM ()

We present a novel adaptive optimization algorithm for black-box multi-objective optimization problems with binary constraints on the foundation of Bayes optimization. Our method is based on probabilistic regression and classification models, which act as a surrogate for the optimization goals and allow us to suggest multiple design points at once in each iteration. The proposed acquisition function is intuitively understandable and can be tuned to the demands of the problems at hand. We also present a novel ellipsoid truncation method to speed up the expected hypervolume calculation in a straightforward way for regression models with a normal probability density. We benchmark our approach with an evolutionary algorithm on multiple test problems.