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Learning Classifier Systems: Looking Back and Glimpsing Ahead

  • Conference paper
Learning Classifier Systems (IWLCS 2006, IWLCS 2007)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4998))

Abstract

Over the recent years, research on Learning Classifier Systems (LCSs) got more and more pronounced and diverse. There have been significant advances of the LCS field on various fronts including system understanding, representations, computational models, and successful applications. In comparison to other machine learning techniques, the advantages of LCSs have become more pronounced: (1) rule-comprehensibility and thus knowledge extraction is straightforward; (2) online learning is possible; (3) local minima are avoided due to the evolutionary learning component; (4) distributed solution representations evolve; or (5) larger problem domains can be handled. After the tenth edition of the International Workshop on LCSs, more than ever before, we are looking towards an exciting future. More diverse and challenging applications, efficiency enhancements, studies of dynamical systems, and applications to cognitive control approaches appear imminent. The aim of this paper is to provide a look back at the LCS field, whereby we place our emphasis on the recent advances. Moreover, we take a glimpse ahead by discussing future challenges and opportunities for successful system applications in various domains.

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Authors and Affiliations

  1. ASAP research group, School of Computer Science, Jubilee Campus, Nottingham, NG8 1BB and Multidisciplinary Centre for Integrative Biology, School of Biosciences, University of Nottingham, Sutton Bonington, LE12 5RD, UK

    Jaume Bacardit

  2. Grup de Recerca en Sistemes Intel.ligents, Enginyeria i Arquitectura La Salle, Universitat Ramon Llull, Quatre Camins 2, 08022, Barcelona, Spain

    Ester Bernadó-Mansilla

  3. Department of Psychology, University of Würzburg, Röntgenring 11, 97070, Würzburg, Germany

    Martin V. Butz

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  1. Jaume Bacardit
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  2. Ester Bernadó-Mansilla
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  3. Martin V. Butz
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Editors and Affiliations

  1. University of Nottingham, School of Computer Science, ASAP research group, Jubilee Campus, Nottingham, NG8 1BB, and Multidisciplinary Centre for Integrative Biology, School of Biosciences, Sutton Bonington, LE12 5RD, UK

    Jaume Bacardit

  2. Enginyeria i Arquitectura La Salle, Gruß de Recerca en Sistemes Intel.ligents, Quatre Camins 2, Universitat Ramon Llull, 08022, Barcelona, Spain

    Ester Bernadó-Mansilla

  3. Department of Psychology, University of Würzburg, Röntgenring 11, 97070, Würzburg, Germany

    Martin V. Butz

  4. Department of Computer Science, University of Bristol, Merchant Venturers Building, Woodland Road, BS8 1UB, Bristol, UK

    Tim Kovacs

  5. Department of Industrial and Enterprise Systems Engineering, Illinois Genetic Algorithms Lab (IlliGAL), University of Illinois at Urbana-Champaign, 104 S. Mathews Avenue, 61801-2996, Urbana, IL, USA

    Xavier Llorà

  6. Tokyo Institute of Technology, 152-8550, tokyo, Japan

    Keiki Takadama

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Bacardit, J., Bernadó-Mansilla, E., Butz, M.V. (2008). Learning Classifier Systems: Looking Back and Glimpsing Ahead. In: Bacardit, J., Bernadó-Mansilla, E., Butz, M.V., Kovacs, T., Llorà, X., Takadama, K. (eds) Learning Classifier Systems. IWLCS IWLCS 2006 2007. Lecture Notes in Computer Science(), vol 4998. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88138-4_1

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