{"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/self-adaptation-of-genetic-operators-through","title":"Self-adaptation of Genetic Operators Through Genetic Programming Techniques","arxiv_id":"1712.06070","date":"2017-12-17","proceeding":null,"authors":["Andres Felipe Cruz Salinas","Jonatan Gomez Perdomo"],"abstract":"Here we propose an evolutionary algorithm that self modifies its operators at\nthe same time that candidate solutions are evolved. This tackles convergence\nand lack of diversity issues, leading to better solutions. Operators are\nrepresented as trees and are evolved using genetic programming (GP) techniques.\nThe proposed approach is tested with real benchmark functions and an analysis\nof operator evolution is provided.","url_abs":"http://arxiv.org/abs/1712.06070v1","url_pdf":"http://arxiv.org/pdf/1712.06070v1.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":"self-adaptation-of-genetic-operators-through","repo_url":"https://github.com/afcruzs/AOEA","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"diversity","task_name":"Diversity"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}