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 $\epsilon$-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.

翻译：Batch Bayesian最佳化(BO)是优化昂贵黑盒功能的成功技术。 Asynconcronous BBO 可以在另一个完成后立即开始新的评估,从而最大限度地实现资源利用。为了最大限度地分配资源,我们开发了一种新的非同步的BO方法,AEGiS(Asyncronous $\epsilon$-Greedy Global Search),该方法结合贪婪的搜索,利用代理公司的预测,利用Thompson的抽样和随机选择来描述开采(覆盖平均预测)和勘探(覆盖后后差差差)之间的交易。我们用经验证明了AEGIS在合成基准问题、元基离子调整问题和现实世界问题方面的功效,表明AEGIS通常比现有不连续的BO方法要强。 当单个工人能够利用预期的改进,其业绩并不比BO差。