{"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/hyper-optimization-with-gaussian-process-and","title":"Hyper-optimization with Gaussian Process and Differential Evolution Algorithm","arxiv_id":"2101.10625","date":"2021-01-26","proceeding":null,"authors":["Jakub Klus","Pavel Grunt","Martin Dobrovolný"],"abstract":"Optimization of problems with high computational power demands is a challenging task. A probabilistic approach to such optimization called Bayesian optimization lowers performance demands by solving mathematically simpler model of the problem. Selected approach, Gaussian Process, models problem using a mixture of Gaussian functions. This paper presents specific modifications of Gaussian Process optimization components from available scientific libraries. Presented modifications were submitted to BlackBox 2020 challenge, where it outperformed some conventionally available optimization libraries.","url_abs":"https://arxiv.org/abs/2101.10625v1","url_pdf":"https://arxiv.org/pdf/2101.10625v1.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":"hyper-optimization-with-gaussian-process-and","repo_url":"https://github.com/Brown-Box/brown-box","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"bayesian-optimization","task_name":"Bayesian Optimization"}],"methods":[{"method_slug":"gaussian-process","method_name":"Gaussian Process"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}