Random Sampling Technique for Overfitting Control in Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7778

@InProceedings{goncalves:2012:EuroGP,

• author = "Ivo Goncalves and Sara Silva and Joana B. Melo and Joao M. B. Carreiras",
• title = "Random Sampling Technique for Overfitting Control in Genetic Programming",
• booktitle = "Proceedings of the 15th European Conference on Genetic Programming, EuroGP 2012",
• year = "2012",
• month = "11-13 " # apr,
• editor = "Alberto Moraglio and Sara Silva and Krzysztof Krawiec and Penousal Machado and Carlos Cotta",
• series = "LNCS",
• volume = "7244",
• publisher = "Springer Verlag",
• address = "Malaga, Spain",
• pages = "218--229",
• organisation = "EvoStar",
• isbn13 = "978-3-642-29138-8",
• URL = "https://old.cisuc.uc.pt/publication/show/2682",
• DOI = "doi:10.1007/978-3-642-29139-5_19",
• size = "12 pages",
• keywords = "genetic algorithms, genetic programming, Over fitting, Generalisation",
• size = "12 pages",
• abstract = "One of the areas of Genetic Programming (GP) that, in comparison to other Machine Learning methods, has seen fewer research efforts is that of generalization. Generalisation is the ability of a solution to perform well on unseen cases. It is one of the most important goals of any Machine Learning method, although in GP only recently has this issue started to receive more attention. In this work we perform a comparative analysis of a particularly interesting configuration of the Random Sampling Technique (RST) against the Standard GP approach. Experiments are conducted on three multidimensional symbolic regression real world datasets, the first two on the pharmacokinetics domain and the third one on the forestry domain. The results show that the RST decreases over fitting on all datasets. This technique also improves testing fitness on two of the three datasets. Furthermore, it does so while producing considerably smaller and less complex solutions. We discuss the possible reasons for the good performance of the RST, as well as its possible limitations.",
• notes = "Part of \cite{Moraglio:2012:GP} EuroGP'2012 held in conjunction with EvoCOP2012 EvoBIO2012, EvoMusArt2012 and EvoApplications2012",

}

Genetic Programming entries for Ivo Goncalves Sara Silva Joana B Melo Joao Manuel de Brito Carreiras

Citations