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arXiv · 2410.23148

Navigating in High-Dimensional Search Space: A Hierarchical Bayesian Optimization Approach

Wenxuan Li·Taiyi Wang·Eiko Yoneki
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Abstract

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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Wenxuan Li, Taiyi Wang, Eiko Yoneki. 2024-10-30. Navigating in High-Dimensional Search Space: A Hierarchical Bayesian Optimization Approach. https://arxiv.org/abs/2410.23148

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