{"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/general-subpopulation-framework-and-taming","title":"General Subpopulation Framework and Taming the Conflict Inside Populations","arxiv_id":"1901.00266","date":"2019-01-02","proceeding":null,"authors":["Danilo Vasconcellos Vargas","Junichi Murata","Hirotaka Takano","Alexandre Claudio Botazzo Delbem"],"abstract":"Structured evolutionary algorithms have been investigated for some time.\nHowever, they have been under-explored specially in the field of\nmulti-objective optimization. Despite their good results, the use of complex\ndynamics and structures make their understanding and adoption rate low. Here,\nwe propose the general subpopulation framework that has the capability of\nintegrating optimization algorithms without restrictions as well as aid the\ndesign of structured algorithms. The proposed framework is capable of\ngeneralizing most of the structured evolutionary algorithms, such as cellular\nalgorithms, island models, spatial predator-prey and restricted mating based\nalgorithms under its formalization. Moreover, we propose two algorithms based\non the general subpopulation framework, demonstrating that with the simple\naddition of a number of single-objective differential evolution algorithms for\neach objective the results improve greatly, even when the combined algorithms\nbehave poorly when evaluated alone at the tests. Most importantly, the\ncomparison between the subpopulation algorithms and their related panmictic\nalgorithms suggests that the competition between different strategies inside\none population can have deleterious consequences for an algorithm and reveal a\nstrong benefit of using the subpopulation framework.\n  The code for SAN, the proposed multi-objective algorithm which has the\ncurrent best results in the hardest benchmark, is available at the following\nhttps://github.com/zweifel/zweifel","url_abs":"http://arxiv.org/abs/1901.00266v1","url_pdf":"http://arxiv.org/pdf/1901.00266v1.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":"general-subpopulation-framework-and-taming","repo_url":"https://github.com/zweifel/zweifel","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"evolutionary-algorithms","task_name":"Evolutionary Algorithms"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}