Surrogate-based Evolutionary Algorithms
Surrogate-Based Evolutionary Algorithms is a class of evolutionary optimization algorithms that employs surrogate models to approximate fitness functions, thereby reducing the computational cost of evaluating candidate solutions in expensive optimization problems.
413
Publications
26.3K
Citations
904
Authors
380
Institutions
Publications per year
2017–2026
270
413
1–5 of 413
904
33
h-index
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380

University of Surrey
Guildford, United Kingdom
26 h-index
52 publications
7.1K citations

Huazhong University of Science and Technology
Wuhan, China
19 h-index
72 publications
4.2K citations

Southern University of Science and Technology
Shenzhen, China
18 h-index
43 publications
2.4K citations

Xidian University
Xi'an, China
17 h-index
33 publications
2K citations
Nanyang Technological University
Singapore, Singapore
15 h-index
34 publications
4.1K citations

Northeastern University
Shenyang, China
14 h-index
44 publications
4.6K citations
Bielefeld University
Bielefeld, Germany
12 h-index
12 publications
472 citations

UNSW Sydney
Sydney, Australia
12 h-index
34 publications
1.3K citations
1–8 of 380
57 publications · 7.2K citations
40
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16 publications · 1.9K citations
16
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21 publications · 760 citations
15
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15 publications · 710 citations
14
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15 publications · 2K citations
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