A Hidden Markov model approach to the problem of heuristic selection in hyper-heuristics with a case study in high school timetabling problems
- Submitting institution
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University of Exeter
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1779
- Type
- D - Journal article
- DOI
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10.1162/EVCO_a_00186
- Title of journal
- Evolutionary Computation
- Article number
- -
- First page
- 473
- Volume
- 25
- Issue
- 3
- ISSN
- 1063-6560
- Open access status
- Compliant
- Month of publication
- January
- Year of publication
- 2017
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
1
- Research group(s)
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-
- Citation count
- 12
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This EPSRC-funded research (EP/K000519/1) is an extension of Best Paper Nominated work at GECCO 2015 which developed a novel sequence-based approach, SSHH, to the problem of heuristic selection. This paper further develops the method for high school timetabling and discovered 9 new (and 4 re-discovered) best-known solutions on international contest problems. SSHH placed 3rd in a Windfarm Optimisation contest at GECCO2015, behind more specific methods, and underpinned the ROADEF 2016 inventory routing contest winner (https://www.roadef.org/challenge/2016/en/finalResults.php). Subsequent publications in the Renewable Energy (10.1016/j.renene.2018.03.052) and Nurse Rostering (10.1016/j.cor.2021.105221) domains demonstrate its versatility.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -