Bayesian optimization (BO) is a popular approach for sample-efficient optimization of black-box objective functions. While BO has been successfully applied to a wide range of scientific applications, traditional approaches to single-objective BO only seek to find a single best solution. This can be a significant limitation in situations where solutions may later turn out to be intractable. For example, a designed molecule may turn out to violate constraints that can only be reasonably evaluated after the optimization process has concluded. To address this issue, we propose Rank-Ordered Bayesian Optimization with Trust-regions (ROBOT) which aims to find a portfolio of high-performing solutions that are diverse according to a user-specified diversity metric. We evaluate ROBOT on several real-world applications and show that it can discover large sets of high-performing diverse solutions while requiring few additional function evaluations compared to finding a single best solution.

翻译：Bayesian 优化 Bayesian (BO) 是一种对黑箱目标功能进行抽样高效优化的流行方法。 虽然BO 成功地应用到广泛的科学应用中,但针对单一目标BO的传统方法只寻求一个单一的最佳解决方案。这在解决方案后来可能变得难以解决的情况下可能是一个重大限制。例如,设计出的分子可能违反限制,而这些限制只能在优化进程结束后才能合理评估。为了解决这一问题,我们提议将Bayesian 优化与信任区域(ROBOT), 目的是根据用户指定的多样性衡量标准找到多种高绩效解决方案组合。 我们评估了多个现实世界应用程序的ROBOT, 并表明它能够发现大量高绩效的多种解决方案,同时需要与找到单一的最佳解决方案相比, 少有额外的功能评估。