Surrogate-assisted Evolutionary Algorithms

Surrogate-Assisted Evolutionary Algorithm is a computational optimization method that belongs to the class of evolutionary algorithms, distinguished by its use of surrogate models to approximate fitness evaluations and thereby reduce the computational cost of solving complex, expensive optimization problems.

383

Publications

24.7K

Citations

825

Authors

337

Institutions

Publications per year

2017–2026

269

Variants

Surrogate-assisted Evolutionary Algorithm

Authors

825

  1. YJ
    Yaochu Jin

    University of Surrey, United Kingdom

    52 publications · 8.2K citations

    35

    h-index

  2. HW
    Handing Wang

    Xidian University, China

    20 publications · 2.2K citations

    19

    h-index

  3. LG
    Liang Gao

    Huazhong University of Science and Technology, China

    17 publications · 906 citations

    16

    h-index

  4. YO
    Yew-Soon Ong

    Nanyang Technological University, Singapore

    15 publications · 1.9K citations

    15

    h-index

  5. TR
    Tapabrata Ray

    UNSW Sydney, Australia

    12 publications · 510 citations

    12

    h-index

1–5 of 825

Venues

  1. IEEE Transactions on Evolutionary Computation

    52 publications · 7K citations

    39

    h-index

  2. IEEE Transactions on Cybernetics

    18 publications · 2K citations

    17

    h-index

  3. Information Sciences

    17 publications · 1.1K citations

    16

    h-index

  4. Applied Soft Computing

    20 publications · 828 citations

    15

    h-index

  5. Swarm and Evolutionary Computation

    15 publications · 1.9K citations

    13

    h-index

1–5