A Surrogate-assisted Reference Vector Guided Evolutionary Algorithm for Computationally Expensive Many-objective Optimization
- Submitting institution
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University of Exeter
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 6345
- Type
- D - Journal article
- DOI
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10.1109/TEVC.2016.2622301
- Title of journal
- IEEE Transactions on Evolutionary Computation
- Article number
- -
- First page
- 129
- Volume
- 22
- Issue
- 1
- ISSN
- 1089-778X
- Open access status
- Deposit exception
- Month of publication
- October
- Year of publication
- 2016
- 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)
-
-
- Citation count
- 72
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The work addresses the challenge of solving computationally expensive problems with many conflicting criteria. It uses Gaussian processes with evolutionary computation and can provide solutions in the least number of evaluations. The method was used in designing the shape of an air intake ventilation system in a tractor manufactured in Valtra, Finland. The solution proposed by the method was approved by expert Pekka Makkonen (makkispekkis@hotmail.com) in the company. The work won the best paper award in IEEE Congress on Evolutionary Computation Conference 2017 (DOI:10.1109/CEC.2017.7969486) and was highlighted in press releases of University of Jyvaskyla (https://www.jyu.fi/ajankohtaista/arkisto/2017/08/tiedote-2017-08-10-12-28-57-779965) and University of Surrey (https://www.surrey.ac.uk/news/surrey-cs-researcher-prof-yaochu-jin-and-his-phd-students-won-two-best-paper-awards).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -