Reactive Search Optimization

Reactive Search Optimization is a heuristic optimization methodology that integrates machine learning mechanisms for self-tuning of parameters and strategies during the search process, distinguishing itself from other metaheuristics by its adaptive, feedback-driven control of search dynamics in real time.

27

Publications

1.4K

Citations

70

Authors

32

Institutions

Publications per year

2017–2026

1

Variants

Rso

Authors

70

  1. AM
    Amir Mosavi

    Obuda University, Hungary

    3 publications · 94 citations

    3

    h-index

  2. YF
    Yoshikazu Fukuyama

    Fuji Electric (Japan), Japan

    3 publications · 105 citations

    3

    h-index

  3. JS
    Junjiro Sugimoto

    Fuji Electric (Japan), Japan

    2 publications · 39 citations

    2

    h-index

  4. RB
    Roberto Battiti

    University of Trento, Italy

    2 publications · 146 citations

    2

    h-index

  5. JW
    J. Wesley Barnes

    The University of Texas at Austin, United States

    2 publications · 397 citations

    2

    h-index

1–5 of 70

Venues

  1. IEEE Transactions on Evolutionary Computation

    2 publications · 146 citations

    2

    h-index

  2. Image and Vision Computing

    1 publications · 12 citations

    1

    h-index

  3. Egyptian Informatics Journal

    1 publications · 10 citations

    1

    h-index

  4. ArXiv.org

    Repository · 1 publications · 25 citations

    1

    h-index

  5. Applied Mathematics

    1 publications · 58 citations

    1

    h-index

1–5