Hyper-heuristics

Hyper-Heuristics is a high-level search methodology that operates on a space of low-level heuristics, distinguishing itself by selecting or generating heuristics dynamically during problem-solving rather than directly solving the problem instance.

283

Publications

13K

Citations

509

Authors

237

Institutions

Publications per year

2017–2026

118

Variants

Hyperheuristics

Authors

509

  1. EB
    Edmund Burke

    University of Nottingham, United Kingdom

    33 publications · 3.4K citations

    23

    h-index

  2. GK
    Graham Kendall

    University of Nottingham, United Kingdom

    28 publications · 2.9K citations

    22

    h-index

  3. EÖ
    Ender Özcan

    University of Nottingham, United Kingdom

    31 publications · 3K citations

    22

    h-index

  4. HT
    Hugo Terashima‐Marín

    Tecnológico de Monterrey, Mexico

    15 publications · 432 citations

    13

    h-index

  5. AK
    Ahmed Kheiri

    University of Nottingham, United Kingdom

    12 publications · 800 citations

    11

    h-index

1–5 of 509

Venues

  1. Information Sciences

    10 publications · 702 citations

    10

    h-index

  2. Applied Soft Computing

    10 publications · 469 citations

    10

    h-index

  3. European Journal of Operational Research

    9 publications · 1.2K citations

    9

    h-index

  4. Expert Systems with Applications

    12 publications · 464 citations

    9

    h-index

  5. IEEE Transactions on Evolutionary Computation

    8 publications · 572 citations

    8

    h-index

1–5