Cooperative co-evolution with differential grouping for large scale optimization
- Submitting institution
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The University of Birmingham
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 24113166
- Type
- D - Journal article
- DOI
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10.1109/TEVC.2013.2281543
- Title of journal
- IEEE Transactions on Evolutionary Computation
- Article number
- 6595612
- First page
- 378
- Volume
- 18
- Issue
- 3
- ISSN
- 1089-778X
- Open access status
- Out of scope for open access requirements
- Month of publication
- June
- Year of publication
- 2014
- URL
-
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- Supplementary information
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- 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
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3
- Research group(s)
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-
- Citation count
- 302
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Optimisation problems with thousands or more parameters (decision variables) are not uncommon (e.g., in neural networks) but pose a big challenge to search algorithms. This work proposes an automatic decomposition approach that can uncover the underlying interaction structure of the problem’s parameters and create subcomponents such that the interdependence between subcomponents is kept to a minimum. Experimental studies have shown that this approach greatly improves the solution quality.
This paper won the 2017 IEEE Transactions on Evolutionary Computation Outstanding Paper Award.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -