Sequential Surrogate Optimization

Sequential Surrogate Optimization is a surrogate-based optimization paradigm in which the acquisition of new sample points and the refinement of the surrogate model occur iteratively in a sequential manner, distinguished from batch or parallel approaches by its strict emphasis on one-at-a-time evaluation decisions guided by infill criteria.

29

Publications

1K

Citations

100

Authors

38

Institutions

Publications per year

2017–2026

14

Variants

Sequential Surrogate-based Optimization

Authors

100

  1. ZJ
    Zhao Jing

    Northwestern Polytechnical University, China

    2 publications · 139 citations

    2

    h-index

  2. LG
    Liangxian Gu

    Northwestern Polytechnical University, China

    2 publications · 139 citations

    2

    h-index

  3. CG
    Chunlin Gong

    Northwestern Polytechnical University, China

    2 publications · 139 citations

    2

    h-index

  4. OV
    Oskar von Stryk

    Technische Universität Darmstadt, Germany

    2 publications · 83 citations

    2

    h-index

  5. HS
    Hajime Sakamoto

    University of Yamanashi Hospital, Japan

    2 publications · 83 citations

    2

    h-index

1–5 of 100

Venues

  1. Structural and Multidisciplinary Optimization

    3 publications · 79 citations

    3

    h-index

  2. 1

    h-index

  3. Structural Safety

    1 publications · 94 citations

    1

    h-index

  4. 1

    h-index

  5. 1

    h-index

1–5