An analysis of heuristic subsequences for offline hyper-heuristic learning
dc.contributor.author | Yates, W | |
dc.contributor.author | Keedwell, EC | |
dc.date.accessioned | 2018-12-18T11:52:45Z | |
dc.date.issued | 2019-01-04 | |
dc.description.abstract | A selection hyper-heuristic is used to minimise the objective functions of a well-known set of benchmark problems. The resulting sequences of low level heuristic selections and objective function values are used to generate a database of heuristic selections. The sequences in the database are broken down into subsequences and the mathematical concept of a logarithmic return is used to discriminate between “effective” subsequences, which tend to decrease the objective value, and “disruptive” subsequences, which tend to increase the objective value. These subsequences are then employed in a sequenced based hyper-heuristic and evaluated on an unseen set of benchmark problems. Empirical results demonstrate that the “effective” subsequences perform significantly better than the “disruptive” subsequences across a number of problem domains with 99% confidence. The identification of subsequences of heuristic selections that can be shown to be effective across a number of problems or problem domains could have important implications for the design of future sequence based hyper-heuristics. | en_GB |
dc.identifier.citation | Published online 4 January 2019 | en_GB |
dc.identifier.doi | 10.1007/s10732-018-09404-7 | |
dc.identifier.uri | http://hdl.handle.net/10871/35187 | |
dc.language.iso | en | en_GB |
dc.publisher | Springer Verlag | en_GB |
dc.rights | © The Author(s) 2019. Open Access. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. | |
dc.title | An analysis of heuristic subsequences for offline hyper-heuristic learning | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2018-12-18T11:52:45Z | |
dc.identifier.issn | 1381-1231 | |
dc.description | This is the final version. Available on open access from Springer Verlag via the DOI in this record | en_GB |
dc.identifier.journal | Journal of Heuristics | en_GB |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en_GB |
dcterms.dateAccepted | 2018-12-14 | |
rioxxterms.version | VoR | en_GB |
rioxxterms.licenseref.startdate | 2018-12-14 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2018-12-17T17:09:44Z | |
refterms.versionFCD | AM | |
refterms.dateFOA | 2019-01-18T16:07:07Z | |
refterms.panel | B | en_GB |
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Open Access.
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.