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：贝叶斯优化是一类基于代理模型的样本高效算法，用于在少量评估预算下优化黑箱问题。其流程本身具有高度可配置性，涉及初始设计、代理模型及采集函数的众多不同设计选择。然而，我们对如何针对具体问题选择合适组件的理解仍十分有限。本研究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挖掘潜在优质解区域之间的权衡。我们提出自调节加权期望改进方法，该方法基于贝叶斯优化的收敛准则，以数据驱动方式实现探索-利用权衡的自适应调节。在COCO基准测试平台的噪声无关黑箱BBOB函数上，所提方法展现出优于人工调优基线的即时性能，可作为任意问题结构的稳健默认选择。该方法的适用性同样延伸至HPOBench。通过SAWEI，我们向实现自适应、数据驱动且稳健的贝叶斯优化设计迈进一步，此类设计能自动根据实际问题调整采样行为。