A tensor-based selection hyper-heuristic for cross-domain heuristic search
- Submitting institution
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University of Nottingham, The
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1321841
- Type
- D - Journal article
- DOI
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10.1016/j.ins.2014.12.020
- Title of journal
- Information Sciences
- Article number
- -
- First page
- 412
- Volume
- 299
- Issue
- -
- ISSN
- 0020-0255
- Open access status
- Out of scope for open access requirements
- Month of publication
- December
- Year of publication
- 2014
- 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
- Yes
- Number of additional authors
-
1
- Research group(s)
-
-
- Citation count
- 21
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This is a revolutionary interdisciplinary study in which tensor analysis of the space of heuristics is used for the first time as a data science approach to improving the performance of a general-purpose optimiser. It has helped shape the international agenda in heuristic optimisation, meta-analytics, and data science. Invited talks have presented this work in Portsmouth, Cardiff, Exeter, Aussois/France, Leuven/Belgium, and Ankara/Turkey. The paper led to the establishment of the EURO working group on `Data Science meets Optimisation', and inspired a startup company (Atlas 4.0) recently founded by Asta in Canada, for which Özcan is scientific advisor.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
