OpenBox: A Generalized Black-box Optimization Service

  • Yang Li ,
  • Yu Shen ,
  • Wentao Zhang ,
  • Yuanwei Chen ,
  • Huai Jun Jiang ,
  • Ming Chao Liu ,
  • Jiawei Jiang ,
  • Jinyang Gao ,
  • ,
  • Zhi Yang ,
  • Ce Zhang ,
  • Bin Cui

ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2021) |

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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.