Cooperative Co-Evolution With Differential Grouping for Large Scale Optimization
- Submitting institution
-
The University of Leeds
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- UOA11-4054
- Type
- D - Journal article
- DOI
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10.1109/tevc.2013.2281543
- Title of journal
- IEEE Transactions on Evolutionary Computation
- Article number
- -
- First page
- 378
- Volume
- 18
- Issue
- 3
- ISSN
- 1089-778X
- Open access status
- Out of scope for open access requirements
- Month of publication
- May
- 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)
-
B - AI (Artificial Intelligence)
- Citation count
- 302
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Solves the problem of identifying variable interaction in black-box continuous functions with a level of accuracy not previously possible (~100%). It derives the differential grouping theorem which is at the core of the interaction identification algorithm. Winner of the Computational Intelligence Society’s best paper award for its novel contribution to large-scale global optimization. Adopted in multiobjective optimization, constrained optimization, and application areas such as civil engineering and big data. Among the top 50 most downloaded papers in IEEE Xplore for 9 consecutive months. The research in this paper played central role in winning two Australian Research Council grants (DP180101170, DP120102205).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
