{"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/evaluating-map-elites-on-constrained","title":"Evaluating MAP-Elites on Constrained Optimization Problems","arxiv_id":"1902.00703","date":"2019-02-02","proceeding":null,"authors":["Stefano Fioravanzo","Giovanni Iacca"],"abstract":"Constrained optimization problems are often characterized by multiple\nconstraints that, in the practice, must be satisfied with different tolerance\nlevels. While some constraints are hard and as such must be satisfied with\nzero-tolerance, others may be soft, such that non-zero violations are\nacceptable. Here, we evaluate the applicability of MAP-Elites to \"illuminate\"\nconstrained search spaces by mapping them into feature spaces where each\nfeature corresponds to a different constraint. On the one hand, MAP-Elites\nimplicitly preserves diversity, thus allowing a good exploration of the search\nspace. On the other hand, it provides an effective visualization that\nfacilitates a better understanding of how constraint violations correlate with\nthe objective function. We demonstrate the feasibility of this approach on a\nlarge set of benchmark problems, in various dimensionalities, and with\ndifferent algorithmic configurations. As expected, numerical results show that\na basic version of MAP-Elites cannot compete on all problems (especially those\nwith equality constraints) with state-of-the-art algorithms that use gradient\ninformation or advanced constraint handling techniques. Nevertheless, it has a\nhigher potential at finding constraint violations vs. objectives trade-offs and\nproviding new problem information. As such, it could be used in the future as\nan effective building-block for designing new constrained optimization\nalgorithms.","url_abs":"http://arxiv.org/abs/1902.00703v4","url_pdf":"http://arxiv.org/pdf/1902.00703v4.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":"evaluating-map-elites-on-constrained","repo_url":"https://github.com/StefanoFioravanzo/MAP-Elites","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}