A Knee Point-Driven Evolutionary Algorithm for Many-Objective Optimization
- Submitting institution
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The University of Surrey
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 9014494_1
- Type
- D - Journal article
- DOI
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10.1109/TEVC.2014.2378512
- Title of journal
- IEEE Transactions on Evolutionary Computation
- Article number
- -
- First page
- 761
- Volume
- 19
- Issue
- 6
- ISSN
- 1089-778X
- Open access status
- Out of scope for open access requirements
- Month of publication
- -
- Year of publication
- 2014
- URL
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-
- Supplementary information
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-
- Request cross-referral to
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- 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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-
- Research group(s)
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- Citation count
- 298
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Knee points contain useful information among the Pareto optimal solutions in multi-objective optimization. It has never been, however, made use of in solving optimization problems. This work identifies knee points during the optimization and prioritize them in selection, thereby accelerating convergence to the Pareto front in optimization of problems with a large number of objective. The algorithm attracted much attention in the research community and has also reported to solve real-world problems such as optimization of clouding resources. The author was invited to give a keynote speech at the 2016 Congress on Evolutionary Computation to present this algorithm.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -