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Random Sampling Technique for Overfitting Control in Genetic Programming

  • Conference paper
Genetic Programming (EuroGP 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7244))

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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. Generalization 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 overfitting 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.

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Author information

Authors and Affiliations

  1. ECOS/CISUC, DEI/FCTUC, University of Coimbra, Portugal

    Ivo Gonçalves & Sara Silva

  2. INESC-ID Lisboa, IST, Technical University of Lisbon, Portugal

    Sara Silva

  3. GeoDES, Tropical Research Institute (IICT), Lisbon, Portugal

    Joana B. Melo & João M. B. Carreiras

Authors
  1. Ivo Gonçalves
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  2. Sara Silva
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  3. Joana B. Melo
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  4. João M. B. Carreiras
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Editor information

Editors and Affiliations

  1. School of Computer Science, University of Birmingham, B15 2TT, Edgbaston, Birmingham, UK

    Alberto Moraglio

  2. INESC-ID Lisboa, Rua Alves Redol 9, 1000-029, Lisboa, Portugal

    Sara Silva

  3. Institute of Computing Science, Poznań University of Technology, Piotrowo 2, 60-965, Poznań, Poland

    Krzysztof Krawiec

  4. Faculty of Sciences and Technology, Department of Informatics Engineering, University of Coimbra, Pólo II - Pinhal de Marrocos, 3030, Coimbra, Portugal

    Penousal Machado

  5. ETSI Informática, Universidad de Málaga, Campus de Teatinos, 29071, Málaga, Spain

    Carlos Cotta

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© 2012 Springer-Verlag Berlin Heidelberg

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Gonçalves, I., Silva, S., Melo, J.B., Carreiras, J.M.B. (2012). Random Sampling Technique for Overfitting Control in Genetic Programming. In: Moraglio, A., Silva, S., Krawiec, K., Machado, P., Cotta, C. (eds) Genetic Programming. EuroGP 2012. Lecture Notes in Computer Science, vol 7244. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29139-5_19

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  • DOI: https://doi.org/10.1007/978-3-642-29139-5_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29138-8

  • Online ISBN: 978-3-642-29139-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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