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).
@article{arxiv.2410.23148,
title = {Navigating in High-Dimensional Search Space: A Hierarchical Bayesian Optimization Approach},
author = {Wenxuan Li and Taiyi Wang and Eiko Yoneki},
journal= {arXiv preprint arXiv:2410.23148},
year = {2025}
}