提出分层贝叶斯优化方法，高效搜索高维空间中的最优解。
Navigating in High-Dimensional Search Space: A Hierarchical Bayesian Optimization Approach
- 用搜索树动态划分高维空间，识别采样潜力高的区域。
- 在合成基准上优于现有方法，数据库配置调优任务中效果显著。
- 适合高维黑箱优化场景，如系统配置调优和超参搜索。
在高维搜索空间中优化黑箱函数对传统贝叶斯优化（BO）而言极具挑战。本文提出一种名为HiBO的新方法，将全局搜索空间划分信息融入局部基于BO的优化器的获取策略中。HiBO采用基于搜索树的全局导航器，自适应地将搜索空间划分为不同采样潜力的子区域；局部优化器利用此全局信息，引导其获取策略聚焦于最有希望的区域。全面实验表明，HiBO在高维合成基准上超越现有最先进方法，并在真实世界数据库管理系统（DBMS）配置调优任务中展现出显著实际有效性。
原文摘要 · Abstract (English)
Optimizing black-box functions in high-dimensional search spaces has been known to be challenging for traditional Bayesian Optimization (BO). In this paper, we introduce HiBO, a novel hierarchical algorithm integrating global-level search space partitioning information into the acquisition strategy of a local BO-based optimizer. HiBO employs a search-tree-based global-level navigator to adaptively split the search space into partitions with different sampling potential. The local optimizer then utilizes this global-level information to guide its acquisition strategy towards most promising regions within the search space. A comprehensive set of evaluations demonstrates that HiBO outperforms state-of-the-art methods in high-dimensional synthetic benchmarks and presents significant practical effectiveness in the real-world task of tuning configurations of database management systems (DBMSs).
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