In this paper, we deal with batch Bayesian Optimization (Bayes-Opt) problems over a box and we propose a novel bi-objective optimization (BOO) acquisition strategy to sample points where to evaluate the objective function. The BOO problem involves the Gaussian Process posterior mean and variance functions, which, in most of the acquisition strategies from the literature, are generally used in combination, frequently through scalarization. However, such scalarization could compromise the Bayes-Opt process performance, as getting the desired trade-off between exploration and exploitation is not trivial in most cases. We instead aim to reconstruct the Pareto front of the BOO problem based on optimizing both the posterior mean as well as the variance, thus generating multiple trade-offs without any a priori knowledge. The reconstruction is performed through the Non-dominated Sorting Memetic Algorithm (NSMA), recently proposed in the literature and proved to be effective in solving hard MOO problems. Finally, we present two clustering approaches, each of them operating on a different space, to select potentially optimal points from the Pareto front. We compare our methodology with well-known acquisition strategies from the literature, showing its effectiveness on a wide set of experiments.
Meta-learning for sample-efficient Bayesian optimisation of fed-batch processes
Department of Computing, Imperial College London, United Kingdom（帝国理工学院伦敦分校计算机系）；BASF SE, Ludwigshafen, Germany（巴斯夫股份有限公司，德国路德维希沙芬）；School of Mathematical Sciences, Lancaster University, United Kingdom（兰卡斯特大学数学科学学院）；Department of Computer Science, University of Oxford, United Kingdom（牛津大学计算机科学系）
Robust Bayesian optimization with reinforcement learned acquisition functions
Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；Collaborative Innovation Center of Novel Software Technology and Industrialization（新型软件技术与产业化协同创新中心）；State Key Laboratory of Aerodynamics（空气动力学国家重点实验室）
