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：贝叶斯优化（BO）是一种对黑箱目标函数进行样本高效优化的常用方法。尽管BO已成功应用于广泛的科学领域，但传统的单目标BO方法仅寻求找到单一最优解。当这些解后续可能被证明不可行时，这可能成为重大局限。例如，设计的分子可能仅在优化过程结束后才能合理评估的约束条件下被视为无效。为解决此问题，我们提出带信任域的排序贝叶斯优化（ROBOT），其目标是根据用户指定的多样性度量，寻找一组高性能且多样化的解决方案。我们在多个实际应用场景中评估ROBOT，结果表明，与仅寻找单一最优解相比，该方法能在仅需少量额外函数评估的情况下，发现大量高性能的多样化解。