Final published version, 373 KB, PDF document
Available under license: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
Final published version
Licence: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
Research output: Contribution to Journal/Magazine › Journal article › peer-review
<mark>Journal publication date</mark> | 2015 |
---|---|
<mark>Journal</mark> | Procedia Engineering |
Issue number | 1 |
Volume | 119 |
Number of pages | 9 |
Pages (from-to) | 1269-1277 |
Publication Status | Published |
<mark>Original language</mark> | English |
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 low level heuristics can be mutation operators or hill climbing algorithms and can include industry expertise. This paper investigates the use of a new hyper-heuristic based on 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.