Stable matching-based selection in evolutionary multiobjective optimization
- Submitting institution
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The University of Birmingham
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 91840356
- Type
- D - Journal article
- DOI
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10.1109/TEVC.2013.2293776
- Title of journal
- IEEE Transactions on Evolutionary Computation
- Article number
- -
- First page
- 909
- Volume
- 18
- Issue
- 6
- ISSN
- 1089-778X
- Open access status
- Out of scope for open access requirements
- Month of publication
- December
- Year of publication
- 2014
- URL
-
-
- 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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4
- Research group(s)
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-
- Citation count
- 176
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- A trend in using evolutionary algorithms to tackle multi-objective optimisation is to decompose a multi-objective problem into a set of scalar subproblems, with each solution in the population responsible for each
subproblem. Naturally, a key issue is how to pair subproblems with solutions. This work used the stable matching theory in economics to build such subproblem-solution pairs. The results were successfully validated on a range of challenging problems.
The paper was published in the top evolutionary computation journal and is a Highly Cited paper (Web of Science). The work directly led to a grant (General Research Fund, Hong Kong, 2015-2018).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -