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European Conference on Genetic Programming

EuroGP 2012: Genetic Programming pp 182–193Cite as

Coevolution in Cartesian Genetic Programming

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7244))

Abstract

Cartesian genetic programming (CGP) is a branch of genetic programming which has been utilized in various applications. This paper proposes to introduce coevolution to CGP in order to accelerate the task of symbolic regression. In particular, fitness predictors which are small subsets of the training set are coevolved with CGP programs. It is shown using five symbolic regression problems that the (median) execution time can be reduced 2–5 times in comparison with the standard CGP.

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Author information

Authors and Affiliations

  1. Faculty of Information Technology, IT4Innovations Centre of Excellence, Brno University of Technology, Božetěchova 2, 612 66, Brno, Czech Republic

    Michaela Šikulová & Lukáš Sekanina

Authors
  1. Michaela Šikulová
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  2. Lukáš Sekanina
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Editor information

Editors and Affiliations

  1. School of Computer Science, University of Birmingham, B15 2TT, Edgbaston, Birmingham, UK

    Alberto Moraglio

  2. INESC-ID Lisboa, Rua Alves Redol 9, 1000-029, Lisboa, Portugal

    Sara Silva

  3. Institute of Computing Science, Poznań University of Technology, Piotrowo 2, 60-965, Poznań, Poland

    Krzysztof Krawiec

  4. Faculty of Sciences and Technology, Department of Informatics Engineering, University of Coimbra, Pólo II - Pinhal de Marrocos, 3030, Coimbra, Portugal

    Penousal Machado

  5. ETSI Informática, Universidad de Málaga, Campus de Teatinos, 29071, Málaga, Spain

    Carlos Cotta

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© 2012 Springer-Verlag Berlin Heidelberg

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Šikulová, M., Sekanina, L. (2012). Coevolution in Cartesian Genetic Programming. In: Moraglio, A., Silva, S., Krawiec, K., Machado, P., Cotta, C. (eds) Genetic Programming. EuroGP 2012. Lecture Notes in Computer Science, vol 7244. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29139-5_16

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  • DOI: https://doi.org/10.1007/978-3-642-29139-5_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29138-8

  • Online ISBN: 978-3-642-29139-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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