Hypervolume-based Multi-objective Bayesian Optimization with Student-t Processes

Joachim van der Herten; Ivo Couckuyt; Tom Dhaene

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arXiv · 1612.00393

Hypervolume-based Multi-objective Bayesian Optimization with Student-t Processes

Joachim van der Herten·Ivo Couckuyt·Tom Dhaene

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Abstract

Student-$t$ processes have recently been proposed as an appealing alternative non-parameteric function prior. They feature enhanced flexibility and predictive variance. In this work the use of Student-$t$ processes are explored for multi-objective Bayesian optimization. In particular, an analytical expression for the hypervolume-based probability of improvement is developed for independent Student-$t$ process priors of the objectives. Its effectiveness is shown on a multi-objective optimization problem which is known to be difficult with traditional Gaussian processes.

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BibTeX RIS

Joachim van der Herten, Ivo Couckuyt, Tom Dhaene. 2016-12-01. Hypervolume-based Multi-objective Bayesian Optimization with Student-t Processes. https://arxiv.org/abs/1612.00393

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