{"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/a-powerful-genetic-algorithm-for-traveling","title":"A Powerful Genetic Algorithm for Traveling Salesman Problem","arxiv_id":"1402.4699","date":"2014-02-19","proceeding":null,"authors":["Shujia Liu"],"abstract":"This paper presents a powerful genetic algorithm(GA) to solve the traveling\nsalesman problem (TSP). To construct a powerful GA, I use edge swapping(ES)\nwith a local search procedure to determine good combinations of building blocks\nof parent solutions for generating even better offspring solutions.\nExperimental results on well studied TSP benchmarks demonstrate that the\nproposed GA is competitive in finding very high quality solutions on instances\nwith up to 16,862 cities.","url_abs":"http://arxiv.org/abs/1402.4699v1","url_pdf":"http://arxiv.org/pdf/1402.4699v1.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":"a-powerful-genetic-algorithm-for-traveling","repo_url":"https://github.com/sugia/GA-for-TSP","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"traveling-salesman-problem","task_name":"Traveling Salesman Problem"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}