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

Authors

904

  1. YJ
    Yaochu Jin

    University of Surrey, United Kingdom

    46 publications · 7.8K citations

    33

    h-index

  2. LG
    Liang Gao

    Huazhong University of Science and Technology, China

    17 publications · 904 citations

    16

    h-index

  3. YO
    Yew-Soon Ong

    Nanyang Technological University, Singapore

    16 publications · 2.5K citations

    16

    h-index

  4. HW
    Handing Wang

    Xidian University, China

    17 publications · 2.1K citations

    16

    h-index

  5. TR
    Tapabrata Ray

    UNSW Sydney, Australia

    11 publications · 439 citations

    11

    h-index

1–5 of 904

Venues

  1. IEEE Transactions on Evolutionary Computation

    57 publications · 7.2K citations

    40

    h-index

  2. IEEE Transactions on Cybernetics

    16 publications · 1.9K citations

    16

    h-index

  3. Applied Soft Computing

    21 publications · 760 citations

    15

    h-index

  4. Information Sciences

    15 publications · 710 citations

    14

    h-index

  5. Swarm and Evolutionary Computation

    15 publications · 2K citations

    13

    h-index

1–5