{"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/solving-the-exponential-growth-of-symbolic","title":"Solving the Exponential Growth of Symbolic Regression Trees in Geometric Semantic Genetic Programming","arxiv_id":"1804.06808","date":"2018-04-18","proceeding":null,"authors":["Joao Francisco B. S. Martins","Luiz Otavio V. B. Oliveira","Luis F. Miranda","Felipe Casadei","Gisele L. Pappa"],"abstract":"Advances in Geometric Semantic Genetic Programming (GSGP) have shown that\nthis variant of Genetic Programming (GP) reaches better results than its\npredecessor for supervised machine learning problems, particularly in the task\nof symbolic regression. However, by construction, the geometric semantic\ncrossover operator generates individuals that grow exponentially with the\nnumber of generations, resulting in solutions with limited use. This paper\npresents a new method for individual simplification named GSGP with Reduced\ntrees (GSGP-Red). GSGP-Red works by expanding the functions generated by the\ngeometric semantic operators. The resulting expanded function is guaranteed to\nbe a linear combination that, in a second step, has its repeated structures and\nrespective coefficients aggregated. Experiments in 12 real-world datasets show\nthat it is not only possible to create smaller and completely equivalent\nindividuals in competitive computational time, but also to reduce the number of\nnodes composing them by 58 orders of magnitude, on average.","url_abs":"http://arxiv.org/abs/1804.06808v1","url_pdf":"http://arxiv.org/pdf/1804.06808v1.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":"solving-the-exponential-growth-of-symbolic","repo_url":"https://github.com/laic-ufmg/GSGP-Red","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"symbolic-regression","task_name":"Symbolic Regression"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}