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Parametrizing Cartesian Genetic Programming: An Empirical Study

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KI 2017: Advances in Artificial Intelligence (KI 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10505))

Abstract

Since its introduction two decades ago, the way researchers parameterized and optimized Cartesian Genetic Programming (CGP) remained almost unchanged. In this work we investigate non-standard parameterizations and optimization algorithms for CGP. We show that the conventional way of using CGP, i.e. configuring it as a single line optimized by an (1+4) Evolutionary Strategies-style search scheme, is a very good choice but that rectangular CGP geometries and more elaborate metaheuristics, such as Simulated Annealing, can lead to faster convergence rates.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Paderborn University, Paderborn, Germany

    Paul Kaufmann

  2. Department of Computer Science, University of Dortmund, Dortmund, Germany

    Roman Kalkreuth

Authors
  1. Paul Kaufmann
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  2. Roman Kalkreuth
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Corresponding author

Correspondence to Paul Kaufmann .

Editor information

Editors and Affiliations

  1. Fakultät für Informatik, Technische Universität Dortmund, Dortmund, Germany

    Gabriele Kern-Isberner

  2. FB Informatik, TU Darmstadt, Darmstadt, Hessen, Germany

    Johannes Fürnkranz

  3. FB Informatik, Universität Koblenz, Koblenz, Rheinland-Pfalz, Germany

    Matthias Thimm

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Kaufmann, P., Kalkreuth, R. (2017). Parametrizing Cartesian Genetic Programming: An Empirical Study. In: Kern-Isberner, G., Fürnkranz, J., Thimm, M. (eds) KI 2017: Advances in Artificial Intelligence. KI 2017. Lecture Notes in Computer Science(), vol 10505. Springer, Cham. https://doi.org/10.1007/978-3-319-67190-1_26

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  • DOI: https://doi.org/10.1007/978-3-319-67190-1_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67189-5

  • Online ISBN: 978-3-319-67190-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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