Kheiri, Ahmed ![]() |
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Abstract
Hyper-heuristics operate at the level above traditional (meta-)heuristics that ‘optimise the optimiser’. These algorithms can combine low level heuristics to create bespoke algorithms for particular classes of problems. The lowlevel heuristics can be mutation operators or hill climbing algorithms and can include industry expertise. This paper investigates the use of a new hyper-heuristic basedon sequence analysis in the biosciences, to develop new optimisers that can outperform conventional evolutionary approaches. It demonstrates that the new algorithms develop high quality solutions on benchmark water distribution network optimisation problems efficiently, and can yield important information about the problem search space.
Item Type: | Article |
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Date Type: | Published Online |
Status: | Published |
Schools: | Mathematics |
Subjects: | Q Science > QA Mathematics |
Publisher: | Elsevier |
ISSN: | 1877-7058 |
Date of First Compliant Deposit: | 30 March 2016 |
Last Modified: | 06 Nov 2024 06:30 |
URI: | https://orca.cardiff.ac.uk/id/eprint/85722 |
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