Efficient Nondomination Level Update Method for Steady-State Evolutionary Multiobjective Optimization
- Submitting institution
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University of Exeter
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 6632
- Type
- D - Journal article
- DOI
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10.1109/TCYB.2016.2621008
- Title of journal
- IEEE Transactions on Cybernetics
- Article number
- -
- First page
- 2838
- Volume
- 47
- Issue
- 9
- ISSN
- 2168-2267
- Open access status
- Compliant
- Month of publication
- November
- Year of publication
- 2016
- 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
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3
- Research group(s)
-
-
- Citation count
- 30
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper explored a novel way to maintain the non-domination level structure in evolutionary multi-objective optimisation. It inspired the evolutionary computation community to pay attention to advanced algorithms and data structures, or efficient parallel implementation, which led to a Workshop on Algorithms and Data Structures for Evolutionary Computation collocated with GECCO, (July 20th, 2016) one of the largest conferences in the evolutionary computation community. It is the foundation of a best paper candidate in GECCO 2017 (DOI: 10.1145/3071178.3071307) and led to a PhD dissertation (awarded in January 2020) at the ITMO University, Russia.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -