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International Conference on Hybrid Information Technology

ICHIT 2011: Convergence and Hybrid Information Technology pp 282–289Cite as

Validation Sets for Evolutionary Curtailment with Improved Generalisation

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 6935))

Abstract

This paper investigates the leveraging of a validation data set with Genetic Programming (GP) to counteract over-fitting. It considers fitness on both training and validation fitness, combined with with an early stopping mechanism to improve generalisation while significantly reducing run times.

The method is tested on six benchmark binary classification data sets. Results of this preliminary investigation suggest that the strategy can deliver equivalent or improved results on test data.

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Author information

Authors and Affiliations

  1. Jeannie Fitzgerald, BDS Group, CSIS Department, University of Limerick, Ireland

    Jeannie Fitzgerald

  2. Conor Ryan, BDS Group, CSIS Department, University of Limerick, Ireland

    Conor Ryan

Authors
  1. Jeannie Fitzgerald
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  2. Conor Ryan
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Editor information

Editors and Affiliations

  1. Computer Engineering Department, Hannam University, 70 Hannamro, Daedeuk-gu, Daejeon, Korea

    Geuk Lee

  2. QinetiQ Company Fellow, Howard Science Limited, 24 Sunrise, WR14 2NJ, Malvern, UK

    Daniel Howard

  3. University of Warsaw, 02-097, Warsaw, Poland

    Dominik Ślęzak

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© 2011 Springer-Verlag Berlin Heidelberg

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Fitzgerald, J., Ryan, C. (2011). Validation Sets for Evolutionary Curtailment with Improved Generalisation. In: Lee, G., Howard, D., Ślęzak, D. (eds) Convergence and Hybrid Information Technology. ICHIT 2011. Lecture Notes in Computer Science, vol 6935. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24082-9_35

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  • DOI: https://doi.org/10.1007/978-3-642-24082-9_35

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24081-2

  • Online ISBN: 978-3-642-24082-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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