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Modelling Evolvability in Genetic Programming

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Genetic Programming (EuroGP 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9594))

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Abstract

We develop a tree-based genetic programming system capable of modelling evolvability during evolution through machine learning algorithms, and exploiting those models to increase the efficiency and final fitness. Existing methods of determining evolvability require too much computational time to be effective in any practical sense. By being able to model evolvability instead, computational time may be reduced. This will be done first by demonstrating the effectiveness of modelling these properties a priori, before expanding the system to show its effectiveness as evolution occurs.

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Authors and Affiliations

  1. Memorial University of Newfoundland, St. John’s, Canada

    Benjamin Fowler & Wolfgang Banzhaf

Authors
  1. Benjamin Fowler
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  2. Wolfgang Banzhaf
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Corresponding author

Correspondence to Benjamin Fowler .

Editor information

Editors and Affiliations

  1. Dalhousie University, Halifax, Nova Scotia, Canada

    Malcolm I. Heywood

  2. University College Dublin, Dublin, Ireland

    James McDermott

  3. Universidade Nova de Lisboa, Lisboa, Portugal

    Mauro Castelli

  4. CIUSC Dept Informatics Engg, University of Coimbra, Coimbra, Portugal

    Ernesto Costa

  5. Edinburgh Napier University, Edinburgh, United Kingdom

    Kevin Sim

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Fowler, B., Banzhaf, W. (2016). Modelling Evolvability in Genetic Programming. In: Heywood, M., McDermott, J., Castelli, M., Costa, E., Sim, K. (eds) Genetic Programming. EuroGP 2016. Lecture Notes in Computer Science(), vol 9594. Springer, Cham. https://doi.org/10.1007/978-3-319-30668-1_14

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  • DOI: https://doi.org/10.1007/978-3-319-30668-1_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-30667-4

  • Online ISBN: 978-3-319-30668-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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