Simple Hyper-Heuristics Control the Neighbourhood Size of Randomised Local Search Optimally for LeadingOnes
- Submitting institution
-
The University of Sheffield
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 2615
- Type
- D - Journal article
- DOI
-
10.1162/evco_a_00258
- Title of journal
- Evolutionary Computation
- Article number
- -
- First page
- 437
- Volume
- 28
- Issue
- 3
- ISSN
- 1063-6560
- Open access status
- Compliant
- Month of publication
- May
- 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
-
2
- Research group(s)
-
A - Algorithms
- Citation count
- 1
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- For the first time it is proven that a simple hyper-heuristic (HH) runs in the best possible expected runtime using the available heuristic ingredients for a standard benchmark function from evolutionary computation. Such rigorous performance statements were requested by the HH community (doi.org/10.1057/jors.2013.71 , JORS2013) and were highlighted as “remarkable” in ISBN 978-3-030-29414-4 (Ch. 6). Led to invited collaboration as 'Chercheur Invité' at Ecole Polytechnique, Paris where the hyperheuristic was further automated to automatically adapt the duration of the learning period (doi.org/10.1145/3205455.3205611).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -