Multi-Target Optimisation via Bayesian Optimisation and Linear Programming

Alistair Shilton, Santu Rana, Sunil Gupta, Svetha Venkatesh
Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:144-154, 2018.

Abstract

In Bayesian Multi-Objective optimisation, expected hypervolume improvement is often used to measure the goodness of candidate solutions. However when there are many objectives the calculation of expected hypervolume improvement can become computationally prohibitive. An alternative approach measures the goodness of a candidate based on the distance of that candidate from the Pareto front in objective space. In this paper we present a novel distance-based Bayesian Many-Objective optimisation algorithm. We demonstrate the efficacy of our algorithm on three problems, namely the DTLZ2 benchmark problem, a hyper-parameter selection problem, and high-temperature creep-resistant alloy design.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR16-shilton18a, title = {Multi-Target Optimisation via {B}ayesian Optimisation and Linear Programming}, author = {Shilton, Alistair and Rana, Santu and Gupta, Sunil and Venkatesh, Svetha}, booktitle = {Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence}, pages = {144--154}, year = {2018}, editor = {Globerson, Amir and Silva, Ricardo}, volume = {R16}, series = {Proceedings of Machine Learning Research}, month = {06--10 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r16/main/assets/shilton18a/shilton18a.pdf}, url = {https://proceedings.mlr.press/r16/shilton18a.html}, abstract = {In Bayesian Multi-Objective optimisation, expected hypervolume improvement is often used to measure the goodness of candidate solutions. However when there are many objectives the calculation of expected hypervolume improvement can become computationally prohibitive. An alternative approach measures the goodness of a candidate based on the distance of that candidate from the Pareto front in objective space. In this paper we present a novel distance-based Bayesian Many-Objective optimisation algorithm. We demonstrate the efficacy of our algorithm on three problems, namely the DTLZ2 benchmark problem, a hyper-parameter selection problem, and high-temperature creep-resistant alloy design.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Multi-Target Optimisation via Bayesian Optimisation and Linear Programming %A Alistair Shilton %A Santu Rana %A Sunil Gupta %A Svetha Venkatesh %B Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2018 %E Amir Globerson %E Ricardo Silva %F pmlr-vR16-shilton18a %I PMLR %P 144--154 %U https://proceedings.mlr.press/r16/shilton18a.html %V R16 %X In Bayesian Multi-Objective optimisation, expected hypervolume improvement is often used to measure the goodness of candidate solutions. However when there are many objectives the calculation of expected hypervolume improvement can become computationally prohibitive. An alternative approach measures the goodness of a candidate based on the distance of that candidate from the Pareto front in objective space. In this paper we present a novel distance-based Bayesian Many-Objective optimisation algorithm. We demonstrate the efficacy of our algorithm on three problems, namely the DTLZ2 benchmark problem, a hyper-parameter selection problem, and high-temperature creep-resistant alloy design. %Z Reissued by PMLR on 04 October 2026.
APA
Shilton, A., Rana, S., Gupta, S. & Venkatesh, S.. (2018). Multi-Target Optimisation via Bayesian Optimisation and Linear Programming. Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R16:144-154 Available from https://proceedings.mlr.press/r16/shilton18a.html. Reissued by PMLR on 04 October 2026.

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