Learning Classifier Systems: Looking Back and Glimpsing Ahead

  • Jaume Bacardit
  • Ester Bernadó-Mansilla
  • Martin V. Butz
Part of the Lecture Notes in Computer Science book series (LNCS, 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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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Jaume Bacardit
    • 1
  • Ester Bernadó-Mansilla
    • 2
  • Martin V. Butz
    • 3
  1. 1.ASAP research group, School of Computer Science, Jubilee Campus, Nottingham, NG8 1BB and Multidisciplinary Centre for Integrative Biology, School of BiosciencesUniversity of NottinghamSutton BoningtonUK
  2. 2.Grup de Recerca en Sistemes Intel.ligents, Enginyeria i Arquitectura La SalleUniversitat Ramon LlullBarcelonaSpain
  3. 3.Department of PsychologyUniversity of WürzburgWürzburgGermany

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