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, ex- pected hypervolume improvement is often used to measure the goodness of candidate solutions. However when there are many ob- jectives the calculation of expected hyper- volume improvement can become computa- tionally 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 bench- mark 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, ex- pected hypervolume improvement is often used to measure the goodness of candidate solutions. However when there are many ob- jectives the calculation of expected hyper- volume improvement can become computa- tionally 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 bench- mark 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, ex- pected hypervolume improvement is often used to measure the goodness of candidate solutions. However when there are many ob- jectives the calculation of expected hyper- volume improvement can become computa- tionally 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 bench- mark 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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