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Bayesian optimisation vs. input uncertainty reduction
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Ungredda, Juan, Pearce , Michael and Branke, Juergen (2022) Bayesian optimisation vs. input uncertainty reduction. ACM Transactions on Modeling and Computer Simulation, 32 (3). 17. doi:10.1145/3510380 ISSN 1558-1195.
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Official URL: https://doi.org/10.1145/3510380
Abstract
Simulators often require calibration inputs estimated from real world data and the estimate can significantly affect simulation output. Particularly when performing simulation optimisation to find an optimal solution, the uncertainty in the inputs significantly affects the quality of the found solution. One remedy is to search for the solution that has the best performance on average over the uncertain range of inputs yielding an optimal compromise solution. We consider the more general setting where a user may choose between either running simulations or instead querying an external data source, improving the input estimate and enabling the search for a more targeted, less compromised solution. We explicitly examine the trade-off between simulation and real data collection in order to find the optimal solution of the simulator with the true inputs. Using a value of information procedure, we propose a novel unified simulation optimisation procedure called Bayesian Information Collection and Optimisation (BICO) that, in each iteration, automatically determines which of the two actions (running simulations or data collection) is more beneficial. We theoretically prove convergence in the infinite budget limit and perform numerical experiments demonstrating that the proposed algorithm is able to automatically determine an appropriate balance between optimisation and data collection.
| Item Type: | Journal Article | ||||||||
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| Subjects: | Q Science > QA Mathematics | ||||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Science > Mathematics | ||||||||
| SWORD Depositor: | Library Publications Router | ||||||||
| Library of Congress Subject Headings (LCSH): | Gaussian processes, Bayesian statistical decision theory, Mathematical optimization | ||||||||
| Journal or Publication Title: | ACM Transactions on Modeling and Computer Simulation | ||||||||
| Publisher: | Association for Computing Machinery (ACM) | ||||||||
| ISSN: | 1558-1195 | ||||||||
| Official Date: | July 2022 | ||||||||
| Dates: |
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| Volume: | 32 | ||||||||
| Number: | 3 | ||||||||
| Article Number: | 17 | ||||||||
| DOI: | 10.1145/3510380 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Access rights to Published version: | Open Access (Creative Commons) | ||||||||
| Date of first compliant deposit: | 6 September 2022 | ||||||||
| Date of first compliant Open Access: | 6 September 2022 | ||||||||
| RIOXX Funder/Project Grant: |
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