{"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/pruning-techniques-for-mixed-ensembles-of","title":"Pruning Techniques for Mixed Ensembles of Genetic Programming Models","arxiv_id":"1801.07668","date":"2018-01-23","proceeding":null,"authors":["Mauro Castelli","Ivo Gonçalves","Luca Manzoni","Leonardo Vanneschi"],"abstract":"The objective of this paper is to define an effective strategy for building\nan ensemble of Genetic Programming (GP) models. Ensemble methods are widely\nused in machine learning due to their features: they average out biases, they\nreduce the variance and they usually generalize better than single models.\nDespite these advantages, building ensemble of GP models is not a\nwell-developed topic in the evolutionary computation community. To fill this\ngap, we propose a strategy that blends individuals produced by standard\nsyntax-based GP and individuals produced by geometric semantic genetic\nprogramming, one of the newest semantics-based method developed in GP. In fact,\nrecent literature showed that combining syntax and semantics could improve the\ngeneralization ability of a GP model. Additionally, to improve the diversity of\nthe GP models used to build up the ensemble, we propose different pruning\ncriteria that are based on correlation and entropy, a commonly used measure in\ninformation theory. Experimental results,obtained over different complex\nproblems, suggest that the pruning criteria based on correlation and entropy\ncould be effective in improving the generalization ability of the ensemble\nmodel and in reducing the computational burden required to build it.","url_abs":"http://arxiv.org/abs/1801.07668v1","url_pdf":"http://arxiv.org/pdf/1801.07668v1.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":"pruning-techniques-for-mixed-ensembles-of","repo_url":"https://github.com/evaboost/evaboost","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"diversity","task_name":"Diversity"}],"methods":[{"method_slug":"pruning","method_name":"Pruning"}],"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}