Dynamic Evolutionary Optimization is an evolutionary optimization paradigm that dynamically adapts its search strategy and problem-solving parameters in response to environmental changes or temporal variations in the objective landscape, distinguishing it from static evolutionary optimization approaches by its explicit handling of time-varying or non-stationary conditions.
339
Publications
18.7K
Citations
845
Authors
370
Institutions
Publications per year
2017–2026
139
Parents
Publications
339
1–5 of 339
Authors
845
- SY
24
h-index
- KC
14
h-index
- YJ
12
h-index
- XY
12
h-index
- OC
9
h-index
1–5 of 845
Institutions
370
De Montfort University
Leicester, United Kingdom
15 h-index
20 publications
1.5K citations
University of Leicester
Leicester, United Kingdom
12 h-index
14 publications
1.4K citations
University of Science and Technology of China
Hefei, China
11 h-index
28 publications
3K citations
City University of Hong Kong
Hong Kong, Hong Kong
10 h-index
12 publications
1.2K citations
Instituto Tecnológico de Tijuana
Tijuana, Mexico
10 h-index
34 publications
1.7K citations
Xidian University
Xi'an, China
9 h-index
23 publications
566 citations
National University of Singapore
Singapore, Singapore
9 h-index
20 publications
2.8K citations
Northeastern University
Shenyang, China
9 h-index
28 publications
2.1K citations
1–8 of 370
Venues
- IEEE Transactions on Evolutionary Computation
22 publications · 3.2K citations
21
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- IEEE Transactions on Cybernetics
13 publications · 2K citations
13
h-index
- Soft Computing
14 publications · 743 citations
12
h-index
- Information Sciences
10 publications · 1.1K citations
10
h-index
- IEEE Access
8 publications · 183 citations
8
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1–5