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arXiv · 2010.07615

Asynchronous ε-Greedy Bayesian Optimisation

George De Ath·Richard M. Everson·Jonathan E. Fieldsend
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Abstract

Batch Bayesian optimisation (BO) is a successful technique for the optimisation of expensive black-box functions. Asynchronous BO can reduce wallclock time by starting a new evaluation as soon as another finishes, thus maximising resource utilisation. To maximise resource allocation, we develop a novel asynchronous BO method, AEGiS (Asynchronous $ε$-Greedy Global Search) that combines greedy search, exploiting the surrogate's mean prediction, with Thompson sampling and random selection from the approximate Pareto set describing the trade-off between exploitation (surrogate mean prediction) and exploration (surrogate posterior variance). We demonstrate empirically the efficacy of AEGiS on synthetic benchmark problems, meta-surrogate hyperparameter tuning problems and real-world problems, showing that AEGiS generally outperforms existing methods for asynchronous BO. When a single worker is available performance is no worse than BO using expected improvement.

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George De Ath, Richard M. Everson, Jonathan E. Fieldsend. 2021-06-11. Asynchronous ε-Greedy Bayesian Optimisation. https://arxiv.org/abs/2010.07615

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