{"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/coco-the-large-scale-black-box-optimization","title":"COCO: The Large Scale Black-Box Optimization Benchmarking (bbob-largescale) Test Suite","arxiv_id":"1903.06396","date":"2019-03-15","proceeding":null,"authors":["Ouassim Elhara","Konstantinos Varelas","Duc Nguyen","Tea Tusar","Dimo Brockhoff","Nikolaus Hansen","Anne Auger"],"abstract":"The bbob-largescale test suite, containing 24 single-objective functions in\ncontinuous domain, extends the well-known single-objective noiseless bbob test\nsuite, which has been used since 2009 in the BBOB workshop series, to large\ndimension. The core idea is to make the rotational transformations R, Q in\nsearch space that appear in the bbob test suite computationally cheaper while\nretaining some desired properties. This documentation presents an approach that\nreplaces a full rotational transformation with a combination of a\nblock-diagonal matrix and two permutation matrices in order to construct test\nfunctions whose computational and memory costs scale linearly in the dimension\nof the problem.","url_abs":"http://arxiv.org/abs/1903.06396v2","url_pdf":"http://arxiv.org/pdf/1903.06396v2.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":"coco-the-large-scale-black-box-optimization","repo_url":"https://github.com/numbbo/coco","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"coco-the-large-scale-black-box-optimization","repo_url":"https://github.com/XAI-liacs/BLADE","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"benchmarking","task_name":"Benchmarking"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1903.06396","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}