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European Conference on Genetic Programming

EuroGP 2009: Genetic Programming pp 73–84Cite as

Mining Evolving Learning Algorithms

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5481))

Abstract

This paper presents an empirical method to identify salient patterns in tree based Genetic Programming. By using an algorithm derived from tree mining techniques and measuring the destructiveness of replacing patterns, we are able to identify those patterns that are responsible for the increased fitness of good individuals. The method is demonstraded on the evolution of learning rules for binary perceptrons.

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Author information

Authors and Affiliations

  1. NCRG, Aston University, Birmingham, UK

    András Joó

Authors
  1. András Joó
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Editor information

Editors and Affiliations

  1. Department of Informatics, Systems and Communication (D.I.S.Co.), University of Milano-Bicocca, viale Sarca 336-U14, 20126, Milano, Italy

    Leonardo Vanneschi

  2. GE Global Research, NY 12309, Niskayuna, USA

    Steven Gustafson

  3. Department of Computer Engineering, University of Coimbra, Polo II - Pinhal de Marrocos, 3030, Coimbra, Portugal

    Alberto Moraglio

  4. Institute of High Performance Computing and Networking, National Research Council of Italy (ICAR - CNR), Via P. Castellino 111, 80131, Napoli, Italy

    Ivanoe De Falco

  5. Wilhelm Schickard Institut für Informatik, Eberhard Karls Universität Tübingen, Abt. Rechnerarchitektur, Sand 1, 72076, Tübingen, Germany

    Marc Ebner

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© 2009 Springer-Verlag Berlin Heidelberg

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Joó, A. (2009). Mining Evolving Learning Algorithms. In: Vanneschi, L., Gustafson, S., Moraglio, A., De Falco, I., Ebner, M. (eds) Genetic Programming. EuroGP 2009. Lecture Notes in Computer Science, vol 5481. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01181-8_7

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  • DOI: https://doi.org/10.1007/978-3-642-01181-8_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-01180-1

  • Online ISBN: 978-3-642-01181-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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