An Analysis of Heuristic Subsequences for Offline Hyper-heuristic Learning
- Submitting institution
-
University of Exeter
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 6412
- Type
- D - Journal article
- DOI
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10.1007/s10732-018-09404-7
- Title of journal
- Journal of Heuristics
- Article number
- -
- First page
- 399
- Volume
- 25
- Issue
- 3
- ISSN
- 1381-1231
- Open access status
- Compliant
- Month of publication
- January
- Year of publication
- 2019
- 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
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1
- Research group(s)
-
-
- Citation count
- 1
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work makes novel use of log returns (usually seen in finance) and a database of heuristic sequences to evaluate the potential for offline learning for hyper-heuristics. This work is the first of its kind to rigorously analyse the potential for offline machine learning of heuristic sequences in the selection hyper-heuristic domain and the findings will have far-reaching implications for the selection and ordering of heuristics for effective search. It has led to further applied work published in Evolutionary Computation (10.1162/evco_a_00277) and water conference CCWI 2019 and has underpinned the award of a PhD (Yates, 2021).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -