Meta-optimization

Meta-Optimization is a meta-learning strategy that involves the systematic adaptation of optimization algorithms or their hyperparameters to improve the learning process of base-level models by learning from past optimization experiences.

656

Publications

46.4K

Citations

2.1K

Authors

886

Institutions

Publications per year

2017–2026

470

Authors

2.1K

  1. LA
    Laith Abualigah

    Universiti Sains Malaysia, Malaysia

    12 publications · 3.3K citations

    12

    h-index

  2. AC
    André C. P. L. F. de Carvalho

    Universidade de São Paulo, Brazil

    10 publications · 487 citations

    10

    h-index

  3. SM
    Seyedali Mirjalili

    Torrens University Australia, Australia

    9 publications · 3.8K citations

    9

    h-index

  4. AK
    A. Kaveh

    Iran University of Science and Technology, Iran

    9 publications · 1.2K citations

    9

    h-index

  5. CS
    Carlos Soares

    Universidade do Porto, Portugal

    8 publications · 379 citations

    8

    h-index

1–5 of 2.1K

Venues

  1. arXiv (Cornell University)

    Repository · 41 publications · 1.5K citations

    20

    h-index

  2. IEEE Access

    26 publications · 1.8K citations

    19

    h-index

  3. Mathematics

    10 publications · 471 citations

    10

    h-index

  4. Applied Soft Computing

    9 publications · 463 citations

    9

    h-index

  5. Information Sciences

    9 publications · 1.3K citations

    9

    h-index

1–5