{"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/hyb-parsimony-a-hybrid-approach-combining","title":"HYB-PARSIMONY: A hybrid approach combining Particle Swarm Optimization and Genetic Algorithms to find parsimonious models in high-dimensional datasets","arxiv_id":null,"date":"2023-12-01","proceeding":"Neurocomputing 2023 12","authors":["J. Divasón","A. Pernia-Espinoza","F.J. Martinez-de-Pison"],"abstract":"The PSO-PARSIMONY methodology (a heuristic for finding accurate and low-complexity models with particle swarm optimization (PSO)) allows obtaining machine learning models with a good balance between accuracy and complexity. However, when the datasets are of high dimensionality, the methodology does not sufficiently reduce the complexity of the models. This paper presents a new hybrid methodology, called HYB-PARSIMONY, that combines PSO with genetic algorithm (GA) based methods. In the early stages of the optimization process, GA methods have a preponderance to accelerate the search for parsimony. Later, PSO becomes more relevant to improve accuracy. This new methodology obtains significant improvements in the search for more accurate and low-complexity models in high-dimensional datasets.","url_abs":"https://www.sciencedirect.com/science/article/pii/S0925231223009633","url_pdf":"https://www.sciencedirect.com/science/article/pii/S0925231223009633/pdfft?md5=caaa6347f1ac35d22da58fa5d21f16a0&pid=1-s2.0-S0925231223009633-main.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":"hyb-parsimony-a-hybrid-approach-combining","repo_url":"https://github.com/jodivaso/hybparsimony","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[{"method_slug":"autoparsimony","method_name":"AutoParsimony"},{"method_slug":"ga","method_name":"GA"}],"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}