The performance of Bayesian optimization (BO), a highly sample-efficient method for expensive black-box problems, is critically governed by the selection of its hyperparameters, including the kernel and acquisition functions. This presents a significant practical challenge: an inappropriate combination of these can lead to poor performance and wasted evaluations. While individual improvements to kernel functions and acquisition functions have been actively explored, the joint and autonomous selection of the best pair of these fundamental hyperparameters has been largely overlooked. This forced practitioners to rely on heuristics or costly manual training. In this work, we propose a framework, BOOST (Bayesian Optimization with Optimal Kernel and Acquisition Function Selection Technique), that automates this selection. BOOST utilizes a simple offline evaluation stage to predict the performance of various kernel-acquisition function pairs and identify the most promising pair before committing to the expensive evaluation process. BOOST is a data-driven strategy selection procedure that evaluates kernel-acquisition pairs based on their empirical performance on the data-in-hand. At each iteration, previously observed points are partitioned into a reference set and a query set. These subsets play roles analogous to training and validation sets in machine learning: the reference set is used for model construction, while the query set represents unseen regions to retrospectively evaluate how effectively each candidate strategy progresses toward the target value. Experiments on synthetic benchmarks and machine learning hyperparameter optimization tasks demonstrate that BOOST consistently improves over fixed-hyperparameter BO and remains competitive with state-of-the-art adaptive methods, highlighting its robustness across diverse landscapes.

翻译：贝叶斯优化（BO）作为一种针对昂贵黑箱问题的高样本效率方法，其性能关键取决于超参数的选择，包括核函数与采集函数。这带来了显著的实际挑战：不适当的组合可能导致性能不佳及评估资源浪费。尽管针对核函数与采集函数的单独改进已得到积极研究，但对这些基础超参数最佳配对进行联合自动选择仍被忽视，迫使实践者依赖启发式方法或昂贵的人工训练。本文提出框架BOOST（贝叶斯优化中核函数与采集函数最优选择技术），实现了自动化选择。BOOST通过简单的离线评估阶段预测不同核函数-采集函数对的性能，在投入昂贵评估过程前识别最有前景的组合。作为一种数据驱动的策略选择流程，BOOST基于候选对在现有数据上的实证表现进行评估。每次迭代中，先前观测点被划分为参考集与查询集，类似机器学习中的训练集与验证集：参考集用于模型构建，而查询集代表未观测区域，用于回顾性评估每个候选策略向目标值推进的有效性。在合成基准和机器学习超参数优化任务上的实验表明，BOOST相比固定超参数BO持续提升性能，且与最先进的自适应方法保持竞争力，彰显其在多样化场景下的鲁棒性。