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Coevolutionary life-time learning

  • Basic Concepts of Evolutionary Computation
  • Conference paper
  • First Online:
Parallel Problem Solving from Nature — PPSN IV (PPSN 1996)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1141))

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Abstract

This work studies the interaction of evolution and learning. It starts from the coevolutionary genetic algorithm (CGA) introduced earlier. Two techniques — life-time fitness evaluation (LTFE) and predator-prey coevolution — boost the genetic search of a CGA. The partial but continuous nature of LTFE allows for an elegant incorporation of life-time learning (LTL) within CGAs. This way, not only the genetic search but also the LTL component focuses on “not yet solved” problems. The performance of the new algorithm is compared with various other algorithms.

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Authors and Affiliations

  1. Research Institute for Knowledge Systems, University of Limburg, Postbus 463, NL-6200, AL Maastricht, The Netherlands

    J. Paredis

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  1. J. Paredis
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Editor information

Hans-Michael Voigt Werner Ebeling Ingo Rechenberg Hans-Paul Schwefel

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© 1996 Springer-Verlag Berlin Heidelberg

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Paredis, J. (1996). Coevolutionary life-time learning. In: Voigt, HM., Ebeling, W., Rechenberg, I., Schwefel, HP. (eds) Parallel Problem Solving from Nature — PPSN IV. PPSN 1996. Lecture Notes in Computer Science, vol 1141. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61723-X_971

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  • DOI: https://doi.org/10.1007/3-540-61723-X_971

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61723-5

  • Online ISBN: 978-3-540-70668-7

  • eBook Packages: Springer Book Archive

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