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European Conference on Genetic Programming

EuroGP 2015: Genetic Programming pp 28–40Cite as

Improving Geometric Semantic Genetic Programming with Safe Tree Initialisation

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9025))

Abstract

Researchers in genetic programming (GP) are increasingly looking to semantic methods to increase the efficacy of search. Semantic methods aim to increase the likelihood that a structural change made in an individual will be correlated with a change in behaviour. Recent work has promoted the use of geometric semantic methods, where offspring are generated within a bounded interval of the parents’ behavioural space. Extensions of this approach use random trees wrapped in logistic functions to parameterise the blending of parents. This paper identifies limitations in the logistic wrapper approach, and suggests an alternative approach based on safe initialisation using interval arithmetic to produce offspring. The proposed method demonstrates greater search performance than using a logistic wrapper approach, while maintaining an ability to produce offspring that exhibit good generalisation capabilities.

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Notes

  1. 1.

    Given that over a third of GP research investigates or uses symbolic regression directly, this is not considered a serious limitation [11].

  2. 2.

    Source code for experiments available at: https://github.com/grantdick/libgsgp.

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Author information

Authors and Affiliations

  1. Department of Information Science, University of Otago, Dunedin, New Zealand

    Grant Dick

Authors
  1. Grant Dick
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Corresponding author

Correspondence to Grant Dick .

Editor information

Editors and Affiliations

  1. Universidade de Coimbra, Coimbra, Portugal

    Penousal Machado

  2. Dalhousie University, Halifax, Nova Scotia, Canada

    Malcolm I. Heywood

  3. University College Dublin, Dublin, Ireland

    James McDermott

  4. Universidade Nova de Lisboa, Lisboa, Portugal

    Mauro Castelli

  5. Universidad de Granada, Granada, Spain

    Pablo García-Sánchez

  6. Aalborg University, Copenhagen, Denmark

    Paolo Burelli

  7. IT University of Copenhagen, Copenhagen, Denmark

    Sebastian Risi

  8. Edinburgh Napier University, Edinburgh, United Kingdom

    Kevin Sim

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© 2015 Springer International Publishing Switzerland

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Dick, G. (2015). Improving Geometric Semantic Genetic Programming with Safe Tree Initialisation. In: Machado, P., et al. Genetic Programming. EuroGP 2015. Lecture Notes in Computer Science(), vol 9025. Springer, Cham. https://doi.org/10.1007/978-3-319-16501-1_3

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  • DOI: https://doi.org/10.1007/978-3-319-16501-1_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16500-4

  • Online ISBN: 978-3-319-16501-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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