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International Conference on Parallel Problem Solving from Nature

PPSN 2022: Parallel Problem Solving from Nature – PPSN XVII pp 19–32Cite as

Gene-pool Optimal Mixing in Cartesian Genetic Programming

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13399))

Abstract

Genetic Programming (GP) can make an important contribution to explainable artificial intelligence because it can create symbolic expressions as machine learning models. Nevertheless, to be explainable, the expressions must not become too large. This may, however, limit their potential to be accurate. The re-use of subexpressions has the unique potential to mitigate this issue. The Genetic Programming Gene-pool Optimal Mixing Evolutionary Algorithm (GP-GOMEA) is a recent model-based GP approach that has been found particularly capable of evolving small expressions. However, its tree representation offers no explicit mechanisms to re-use subexpressions. By contrast, the graph representation in Cartesian GP (CGP) is natively capable of re-use. For this reason, we introduce CGP-GOMEA, a variant of GP-GOMEA that uses graphs instead of trees. We experimentally compare various configurations of CGP-GOMEA with GP-GOMEA and find that CGP-GOMEA performs on par with GP-GOMEA on three common datasets. Moreover, CGP-GOMEA is found to produce models that re-use subexpressions more often than GP-GOMEA uses duplicate subexpressions. This indicates that CGP-GOMEA has unique added potential, allowing to find even smaller expressions than GP-GOMEA with similar accuracy.

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Notes

  1. 1.

    Code and data can be found at https://github.com/matigekunstintelligentie/CGP-GOMEA.

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Acknowledgement

This research is part of the research programme Open Competition Domain Science-KLEIN with project number OCENW.KLEIN.111, which is financed by the Dutch Research Council (NWO). We further thank the Maurits en Anna de Kock Foundation for financing a high-performance computing system. We also thank Marco Virgolin aiding in implementing GP-GOMEA, and Dazhuang Liu and Evi Sijben for their fruitful discussions and reviews.

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Authors and Affiliations

  1. Centrum Wiskunde & Informatica, Amsterdam, The Netherlands

    Joe Harrison & Peter A. N. Bosman

  2. Delft University of Technology, Delft, The Netherlands

    Joe Harrison & Peter A. N. Bosman

  3. Leiden University Medical Center, Leiden, The Netherlands

    Tanja Alderliesten

Authors
  1. Joe Harrison
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  2. Tanja Alderliesten
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  3. Peter A. N. Bosman
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Corresponding author

Correspondence to Joe Harrison .

Editor information

Editors and Affiliations

  1. TU Dortmund, Dortmund, Germany

    Günter Rudolph

  2. Leiden University, Leiden, The Netherlands

    Anna V. Kononova

  3. Shinshu University, Nagano, Japan

    Hernán Aguirre

  4. Technische Universität Dresden, Dresden, Germany

    Pascal Kerschke

  5. University of Stirling, Stirling, UK

    Gabriela Ochoa

  6. Jožef Stefan Institute, Ljubljana, Slovenia

    Tea Tušar

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Harrison, J., Alderliesten, T., Bosman, P.A.N. (2022). Gene-pool Optimal Mixing in Cartesian Genetic Programming. In: Rudolph, G., Kononova, A.V., Aguirre, H., Kerschke, P., Ochoa, G., Tušar, T. (eds) Parallel Problem Solving from Nature – PPSN XVII. PPSN 2022. Lecture Notes in Computer Science, vol 13399. Springer, Cham. https://doi.org/10.1007/978-3-031-14721-0_2

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  • DOI: https://doi.org/10.1007/978-3-031-14721-0_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-14720-3

  • Online ISBN: 978-3-031-14721-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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