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Templar – A Framework for Template-Method Hyper-Heuristics

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Genetic Programming (EuroGP 2015)

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

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Abstract

In this work we introduce Templar, a software framework for customising algorithms via the generative technique of template-method hyper-heuristics. We first discuss the need for such an approach, presenting Quicksort as an example. We provide a functional definition of template-method hyper-heuristics, describe how this is implemented by Templar, and show how Templar may be invoked using simple client-code. Finally, we describe experiments using Templar to define a ‘hyper-quicksort’ with the aim of reducing power consumption—the results demonstrate that the generated algorithm has significantly improved performance on the test set.

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Notes

  1. 1.

    docs.oracle.com/javase/tutorial/java/javaOO/lambdaexpressions.html.

  2. 2.

    In order to provide the highest possible accuracy, Jalen was modified to run from within the Java Virtual Machine, rather than as an external Java Agent.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of York, York, YO10 5GH, UK

    Jerry Swan & Nathan Burles

Authors
  1. Jerry Swan
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  2. Nathan Burles
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Corresponding author

Correspondence to Nathan Burles .

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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Swan, J., Burles, N. (2015). Templar – A Framework for Template-Method Hyper-Heuristics. 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_17

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  • DOI: https://doi.org/10.1007/978-3-319-16501-1_17

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  • 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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