Templar – A Framework for Template-Method Hyper-Heuristics
Research output: Chapter in Book/Report/Conference proceeding › Chapter (peer-reviewed) › peer-review
Title of host publication | Genetic Programming |
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Date | Published - 2015 |
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Pages | 205-216 |
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Publisher | Springer International Publishing |
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Place of Publication | Cham |
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Editors | Penousal Machado, Malcolm Heywood, James McDermott, Mauro Castelli, Pablo Garcia-Sanchez, Paolo Burelli, Sebastian Risi, Kevin Sim |
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Original language | English |
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ISBN (Electronic) | 978-3-319-16501-1 |
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ISBN (Print) | 978-3-319-16500-4 |
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Name | Lecture Notes in Computer Science |
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Publisher | Springer |
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Volume | 9025 |
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ISSN (Print) | 0302-9743 |
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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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