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On Dynamical Genetic Programming: Random Boolean Networks in Learning Classifier Systems

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Genetic Programming (EuroGP 2009)

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

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Abstract

Many representations have been presented to enable the effective evolution of computer programs. Turing was perhaps the first to present a general scheme by which to achieve this end. Significantly, Turing proposed a form of discrete dynamical system and yet dynamical representations remain almost unexplored within genetic programming. This paper presents results from an initial investigation into using a simple dynamical genetic programming representation within a Learning Classifier System. It is shown possible to evolve ensembles of dynamical Boolean function networks to solve versions of the well-known multiplexer problem. Both synchronous and asynchronous systems are considered.

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Authors and Affiliations

  1. Department of Computer Science, University of the West of England, Bristol, BS16 1QY, UK

    Larry Bull & Richard Preen

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  1. Larry Bull
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  2. Richard Preen
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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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Bull, L., Preen, R. (2009). On Dynamical Genetic Programming: Random Boolean Networks in Learning Classifier Systems. 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_4

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  • DOI: https://doi.org/10.1007/978-3-642-01181-8_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-01180-1

  • Online ISBN: 978-3-642-01181-8

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