A Dynamic Multiarmed Bandit-Gene Expression Programming Hyper-Heuristic for Combinatorial Optimization Problems
- Submitting institution
-
University of Nottingham, The
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1319175
- Type
- D - Journal article
- DOI
-
10.1109/TCYB.2014.2323936
- Title of journal
- IEEE Transactions on Cybernetics
- Article number
- -
- First page
- 217
- Volume
- 45
- Issue
- 2
- ISSN
- 2168-2275
- Open access status
- Out of scope for open access requirements
- Month of publication
- June
- 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
- No
- Number of additional authors
-
3
- Research group(s)
-
-
- Citation count
- 56
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Evolutionary algorithms are often manually designed for specific problems, and thus do not generalise well across different optimisation problems. This work develops a dynamic strategy in general heuristic algorithms which obtained the best results for highly different problems, while most algorithms developed in the literature are for a single combinatorial optimisation problem. The dynamic strategy is not specifically designed for particular problems, and the common and same algorithm can therefore be easily and quickly adapted to other problem domains.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
