Evolutionary n-level hypergraph partitioning with adaptive coarsening
- Submitting institution
-
University of the West of England, Bristol
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 852534
- Type
- D - Journal article
- DOI
-
10.1109/TEVC.2019.2896951
- Title of journal
- IEEE Transactions on Evolutionary Computation
- Article number
- -
- First page
- 962
- Volume
- 23
- Issue
- 6
- ISSN
- 1089-778X
- Open access status
- Compliant
- Month of publication
- February
- Year of publication
- 2019
- URL
-
http://dx.doi.org/10.1109/TEVC.2019.2896951
- 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)
-
-
- Citation count
- 0
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Originality: A new method for detecting appropriate level of problem aggregation based on rate of information decay is coupled with metaheuristic design based on search landscape analysis to improve the state of the art.
Rigour: The approach to algorithm design based on rigourous landscape-analysis contrasts with, and significantly outperforms, common “intuition” based heuristic design choices.
Significance: This paper led to invited talk at SAINT Worksop on ‘Evolutionary Computation and its Applications, SUSTC, Shenzen, 2019. Software embedding the algorithms is in final testing for deployment within the toolchain used by Office for National Statistics for publishing datasets such as employment statistics.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
