Black-box optimization (BBO) has a broad range of applications, including automatic machine learning, engineering, physics, and experimental design. However, it remains a challenge for users to apply BBO methods to their problems at hand with existing software packages, in terms of applicability, performance, and efficiency. In this paper, we build OpenBox, an open-source and general-purpose BBO service with improved usability. The modular design behind OpenBox also facilitates flexible abstraction and optimization of basic BBO components that are common in other existing systems. OpenBox is distributed, fault-tolerant, and scalable. To improve efficiency, OpenBox further utilizes "algorithm agnostic" parallelization and transfer learning. Our experimental results demonstrate the effectiveness and efficiency of OpenBox compared to existing systems.
@article{arxiv.2106.00421,
title = {OpenBox: A Generalized Black-box Optimization Service},
author = {Yang Li and Yu Shen and Wentao Zhang and Yuanwei Chen and Huaijun Jiang and Mingchao Liu and Jiawei Jiang and Jinyang Gao and Wentao Wu and Zhi Yang and Ce Zhang and Bin Cui},
journal= {arXiv preprint arXiv:2106.00421},
year = {2021}
}