{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/openbox-a-generalized-black-box-optimization","title":"OpenBox: A Generalized Black-box Optimization Service","arxiv_id":"2106.00421","date":"2021-06-01","proceeding":null,"authors":["Yang Li","Yu Shen","Wentao Zhang","Yuanwei Chen","Huaijun Jiang","Mingchao Liu","Jiawei Jiang","Jinyang Gao","Wentao Wu","Zhi Yang","Ce Zhang","Bin Cui"],"abstract":"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.","url_abs":"https://arxiv.org/abs/2106.00421v3","url_pdf":"https://arxiv.org/pdf/2106.00421v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"openbox-a-generalized-black-box-optimization","repo_url":"https://github.com/PKU-DAIR/open-box","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"openbox-a-generalized-black-box-optimization","repo_url":"https://github.com/thomas-young-2013/lite-bo","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"openbox-a-generalized-black-box-optimization","repo_url":"https://github.com/PKU-DAIR/mindware","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"openbox-a-generalized-black-box-optimization","repo_url":"https://github.com/thomas-young-2013/mindware","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"openbox-a-generalized-black-box-optimization","repo_url":"https://github.com/thomas-young-2013/open-box","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"openbox-a-generalized-black-box-optimization","repo_url":"https://github.com/thomas-young-2013/soln-ml","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"experimental-design","task_name":"Experimental Design"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[{"method_slug":null,"method_name":null}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2106.00421","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}