Asynchronous ε-Greedy Bayesian Optimisation

Bayesian Optimisation (BO) is a popular surrogate model-based approach for optimising expensive black-box functions. In order to reduce optimisation wallclock time, parallel evaluation of the black-box function has been proposed. Asynchronous BO allows for a new evaluation to be started as soon as another finishes, thus maximising utilisation of evaluation workers. We present AEGiS (Asynchronous $\epsilon$-Greedy Global Search), an asynchronous BO method that, with probability $2\epsilon$, performs either Thompson sampling or random selection from the approximate Pareto set trading-off between exploitation (surrogate mean prediction) and exploration (surrogate posterior variance). The remaining $1-2\epsilon$ of moves exploit the surrogate's mean prediction. Results on fifteen synthetic benchmark problems, three meta-surrogate hyperparameter tuning problems and two robot pushing problems show that AEGiS generally outperforms existing methods for asynchronous BO. When a single worker is available performance is no worse than BO using expected improvement. We also verify the importance of each of the three components in an ablation study, as well as comparing Pareto set selection to selection from the entire feasible problem domain, finding that the former is vastly superior.