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We present a novel adaptive optimization algorithm for black-boxmulti-objective optimization problems with binary constraints on the foundationof Bayes optimization. Our method is based on probabilistic regression andclassification models, which act as a surrogate for the optimization goals andallow us to suggest multiple design points at once in each iteration. Theproposed acquisition function is intuitively understandable and can be tuned tothe demands of the problems at hand. We also present a novel ellipsoidtruncation method to speed up the expected hypervolume calculation in astraightforward way for regression models with a normal probability density. Webenchmark our approach with an evolutionary algorithm on multiple testproblems.

Adaptive Sampling of Pareto Frontiers with Binary Constraints Using Regression and Classification