Bayesian Optimization (BO) is a class of surrogate-based, sample-efficient algorithms for optimizing black-box problems with small evaluation budgets. The BO pipeline itself is highly configurable with many different design choices regarding the initial design, surrogate model, and acquisition function (AF). Unfortunately, our understanding of how to select suitable components for a problem at hand is very limited. In this work, we focus on the definition of the AF, whose main purpose is to balance the trade-off between exploring regions with high uncertainty and those with high promise for good solutions. We propose Self-Adjusting Weighted Expected Improvement (SAWEI), where we let the exploration-exploitation trade-off self-adjust in a data-driven manner, based on a convergence criterion for BO. On the noise-free black-box BBOB functions of the COCO benchmarking platform, our method exhibits a favorable any-time performance compared to handcrafted baselines and serves as a robust default choice for any problem structure. The suitability of our method also transfers to HPOBench. With SAWEI, we are a step closer to on-the-fly, data-driven, and robust BO designs that automatically adjust their sampling behavior to the problem at hand.

翻译：贝叶斯优化（BO）是一类基于代理模型的样本高效算法，用于解决具有小评估预算的黑箱问题。BO流程本身具有高度可配置性，涉及初始设计、代理模型和采集函数（AF）等多种设计选择。然而，我们目前对如何为特定问题选择合适组件的理解十分有限。本文聚焦于采集函数的定义，其主要功能是平衡探索高不确定性区域与开发高潜力优质解区域之间的权衡。我们提出自适应加权期望改进（SAWEI）方法，该方法基于BO收敛准则，能够以数据驱动的方式实现探索-开发权衡的自适应调节。在COCO基准平台的噪声无BBOB函数上，我们的方法相比人工设计基线展现出更优的即时性能，并可作为面向任意问题结构的稳健默认选择。该方法的适用性还可迁移至HPOBench。通过SAWEI，我们在实现即时、数据驱动且鲁棒的BO设计方面迈出了重要一步，此类设计能自动针对手头问题调整其采样行为。