Trust Region-Based Bayesian Optimisation to Discover Diverse Solutions
Files
(Published version)
Date
2025
Authors
Perera, K.K.
Neumann, F.
Neumann, A.
Editors
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Conference paper
Citation
Proceedings of the 18th ACM/SIGEVO Conference on Foundations of Genetic Algorithms (FOGA 2025), 2025, pp.214-225
Statement of Responsibility
Kokila Kasuni Perera, Frank Neumann, Aneta Neumann
Conference Name
18th ACM/SIGEVO Conference on Foundations of Genetic Algorithms (FOGA) (27 Aug 2025 - 29 Aug 2025 : Leiden, The Netherlands)
Abstract
Bayesian optimisation (BO) is a surrogate-based optimisation technique that efficiently solves expensive black-box functions with small evaluation budgets. Recent studies consider trust regions to improve the scalability of BO approaches when the problem space scales to more dimensions. Motivated by this research, we explore the effectiveness of trust region-based BO algorithms for diversity optimisation in different dimensional black box problems. We propose diversity optimisation approaches extending TuRBO1, which is the first BO method that uses a trust region-based approach for scalability. We extend TuRBO1 as divTuRBO1, which finds an optimal solution while maintaining a given distance threshold relative to a reference solution set. We propose two approaches to find diverse solutions for black-box functions by combining divTuRBO1 runs in a sequential and an interleaving fashion. We conduct experimental investigations on the proposed algorithms and compare their performance with that of the baseline method, ROBOT (rank-ordered Bayesian optimisation with trust regions). We evaluate proposed algorithms on benchmark functions with dimensions 2 to 20. Experimental investigations demonstrate that the proposed methods perform well, particularly in larger dimensions, even with a limited evaluation budget.
School/Discipline
Dissertation Note
Provenance
Description
Access Status
Rights
© 2025 Copyright held by the owner/author(s). This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.