By the same authors

Templar – A Framework for Template-Method Hyper-Heuristics

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review

Published copy (DOI)



Publication details

Title of host publicationGenetic Programming
DatePublished - 2015
PublisherSpringer International Publishing
Place of PublicationCham
EditorsPenousal Machado, Malcolm Heywood, James McDermott, Mauro Castelli, Pablo Garcia-Sanchez, Paolo Burelli, Sebastian Risi, Kevin Sim
Original languageEnglish
ISBN (Electronic)978-3-319-16501-1
ISBN (Print)978-3-319-16500-4

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743


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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