Journal article 710 views 199 downloads
Greed Is Good: Exploration and Exploitation Trade-offs in Bayesian Optimisation
George De Ath,
Richard M. Everson,
Jonathan E. Fieldsend,
Alma Rahat 
ACM Transactions on Evolutionary Learning and Optimization, Volume: 1, Issue: 1, Pages: 1 - 22
Swansea University Author:
Alma Rahat
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DOI (Published version): 10.1145/3425501
Abstract
The performance of acquisition functions for Bayesian optimisation to locate the global optimum of continuous functions is investigated in terms of the Pareto front between exploration and exploitation. We show that Expected Improvement (EI) and the Upper Confidence Bound (UCB) always select solutio...
| Published in: | ACM Transactions on Evolutionary Learning and Optimization |
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| ISSN: | 2688-299X 2688-3007 |
| Published: |
Association for Computing Machinery (ACM)
2021
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa55241 |
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| Abstract: |
The performance of acquisition functions for Bayesian optimisation to locate the global optimum of continuous functions is investigated in terms of the Pareto front between exploration and exploitation. We show that Expected Improvement (EI) and the Upper Confidence Bound (UCB) always select solutions to be expensively evaluated on the Pareto front, but Probability of Improvement is not guaranteed to do so and Weighted Expected Improvement does so only for a restricted range of weights. We introduce two novel ϵ-greedy acquisition functions. Extensive empirical evaluation of these together with random search, purely exploratory, and purely exploitative search on 10 benchmark problems in 1 to 10 dimensions shows that ϵ-greedy algorithms are generally at least as effective as conventional acquisition functions (e.g. EI and UCB), particularly with a limited budget. In higher dimensions ϵ-greedy approaches are shown to have improved performance over conventional approaches. These results are borne out on a real world computational fluid dynamics optimisation problem and a robotics active learning problem. Our analysis and experiments suggest that the most effective strategy, particularly in higher dimensions, is to be mostly greedy, occasionally selecting a random exploratory solution. |
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| Item Description: |
Supplemental Material available as a zip file from acm.org via https://dl.acm.org/doi/10.1145/3425501 |
| College: |
Faculty of Science and Engineering |
| Issue: |
1 |
| Start Page: |
1 |
| End Page: |
22 |