OpenBox: A Generalized Black-box Optimization Service
This work addresses usability and efficiency issues in black-box optimization for users in fields like machine learning and engineering, though it appears incremental as it builds on existing BBO components with modular improvements.
The authors tackled the challenge of applying black-box optimization (BBO) methods to real-world problems by building OpenBox, an open-source and general-purpose BBO service that improves usability, efficiency, and scalability, with experimental results showing its effectiveness and efficiency compared to existing systems.
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.