Surrogate-based Genetic Algorithms

Surrogate-Based Genetic Algorithms is a type of genetic algorithm that incorporates surrogate models to approximate fitness evaluations, thereby reducing the computational cost of optimization by guiding the search process with predictions from the surrogate.

38

Publications

2.6K

Citations

98

Authors

59

Institutions

Publications per year

2017–2026

23

Authors

98

  1. AC
    Afonso Celso de Castro Lemonge

    Universidade Federal de Juiz de Fora, Brazil

    3 publications · 53 citations

    3

    h-index

  2. HJ
    Hélio J. C. Barbosa

    Laboratório Nacional de Computação Científica, Brazil

    3 publications · 53 citations

    3

    h-index

  3. LG
    Liang Gao

    Huazhong University of Science and Technology, China

    2 publications · 59 citations

    2

    h-index

  4. YJ
    Yaochu Jin

    University of Surrey, United Kingdom

    2 publications · 1.5K citations

    2

    h-index

  5. SN
    Sławomir Nowaczyk

    Halmstad University, Sweden

    2 publications · 68 citations

    2

    h-index

1–5 of 98

Venues

  1. Information Sciences

    3 publications · 87 citations

    3

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  2. Expert Systems with Applications

    2 publications · 80 citations

    2

    h-index

  3. Swarm and Evolutionary Computation

    2 publications · 1.5K citations

    2

    h-index

  4. IEEE Transactions on Cybernetics

    2 publications · 123 citations

    2

    h-index

  5. Fuel

    2 publications · 79 citations

    2

    h-index

1–5