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Combining Semantically-Effective and Geometric Crossover Operators for Genetic Programming

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Parallel Problem Solving from Nature – PPSN XIII (PPSN 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8672))

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Abstract

We propose a way to combine two distinct general patterns for designing semantic crossover operators for genetic programming: geometric semantic approach and semantically-effective approach. In the experimental part we show the synergistic effects of combining these two approaches, which we explain by a major fraction of crossover acts performed by geometric semantic crossover operators being semantically ineffective. The results of the combined approach show significant improvement of performance and high resistance to a premature convergence.

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Author information

Authors and Affiliations

  1. Institute of Computing Science, Poznan University of Technology, Poznań, Poland

    Tomasz P. Pawlak

Authors
  1. Tomasz P. Pawlak
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Editor information

Editors and Affiliations

  1. Faculty of Computer and Engineering Sciences, Cologne University of Applied Sciences, Steinmüllerallee 1, 51643, Gummersbach, Germany

    Thomas Bartz-Beielstein

  2. Warwick Business School, University of Warwick, CV8 2SY, Coventry, UK

    Jürgen Branke

  3. Department of Intelligent Systems, JožefStefan Institute, Jamova cesta 39, 1000, Ljubljana, Slovenia

    Bogdan Filipič

  4. Department of Computer Science and Creative Technologies, University of the West of England, BS16 1QY, Bristol, UK

    Jim Smith

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Pawlak, T.P. (2014). Combining Semantically-Effective and Geometric Crossover Operators for Genetic Programming. In: Bartz-Beielstein, T., Branke, J., Filipič, B., Smith, J. (eds) Parallel Problem Solving from Nature – PPSN XIII. PPSN 2014. Lecture Notes in Computer Science, vol 8672. Springer, Cham. https://doi.org/10.1007/978-3-319-10762-2_45

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  • DOI: https://doi.org/10.1007/978-3-319-10762-2_45

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-10761-5

  • Online ISBN: 978-3-319-10762-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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