{"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/new-heuristics-for-parallel-and-scalable","title":"New Heuristics for Parallel and Scalable Bayesian Optimization","arxiv_id":"1807.00373","date":"2018-07-01","proceeding":null,"authors":["Ran Rubin"],"abstract":"Bayesian optimization has emerged as a strong candidate tool for global\noptimization of functions with expensive evaluation costs. However, due to the\ndynamic nature of research in Bayesian approaches, and the evolution of\ncomputing technology, using Bayesian optimization in a parallel computing\nenvironment remains a challenge for the non-expert. In this report, I review\nthe state-of-the-art in parallel and scalable Bayesian optimization methods. In\naddition, I propose practical ways to avoid a few of the pitfalls of Bayesian\noptimization, such as oversampling of edge parameters and over-exploitation of\nhigh performance parameters. Finally, I provide relatively simple, heuristic\nalgorithms, along with their open source software implementations, that can be\nimmediately and easily deployed in any computing environment.","url_abs":"http://arxiv.org/abs/1807.00373v2","url_pdf":"http://arxiv.org/pdf/1807.00373v2.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":"new-heuristics-for-parallel-and-scalable","repo_url":"https://github.com/ranr01/miniBOP","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"bayesian-optimization","task_name":"Bayesian Optimization"},{"task_slug":"global-optimization","task_name":"global-optimization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}