A Steady-State and Generational Evolutionary Algorithm for Dynamic Multiobjective Optimization
- Submitting institution
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De Montfort University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 11141
- Type
- D - Journal article
- DOI
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10.1109/TEVC.2016.2574621
- Title of journal
- IEEE Transactions on Evolutionary Computation
- Article number
- -
- First page
- 65
- Volume
- 21
- Issue
- 1
- ISSN
- 1089-778X
- Open access status
- Compliant
- Month of publication
- -
- 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
-
1
- Research group(s)
-
-
- Citation count
- 66
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- As one of a few pioneering works, this paper proposes an efficient steady-state and generational evolutionary algorithm (SGEA) for solving DMOPs. Since its publication, the work has greatly promoted the research area of evolutionary dynamic multi-objective optimization and SGEA has been used as the baseline algorithm for comparison for solving DMOPs in 10+ papers. This work has led to invited talks, including a keynote at ICSI 2018 [150+ participants] and a tutorial at IEEE SSCI 2018 [600+ participants].
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -