Genetic Algorithm Parameter Tuning

Genetic Algorithm Parameter Tuning is the systematic process of selecting and adjusting the operational parameters of a genetic algorithm—such as population size, crossover and mutation rates, and selection mechanisms—to optimize its performance in terms of convergence speed, solution quality, and robustness across problem instances.

1.3K

Publications

69.8K

Citations

3.4K

Authors

1.2K

Institutions

Publications per year

2017–2026

295

Variants

Ga Parameter Tuning

Authors

3.4K

  1. FH
    Francisco Herrera

    Universidad de Granada, Spain

    14 publications · 3.1K citations

    14

    h-index

  2. DE
    David E. Goldberg

    University of Illinois Urbana-Champaign, United States

    9 publications · 290 citations

    9

    h-index

  3. ST
    Seyed Taghi Akhavan Niaki

    Sharif University of Technology, Iran

    8 publications · 232 citations

    8

    h-index

  4. YA
    Y.L. Abdel-Magid

    King Fahd University of Petroleum and Minerals, Saudi Arabia

    7 publications · 371 citations

    7

    h-index

  5. AE
    A. E. Eiben

    Vrije Universiteit Amsterdam, Netherlands

    6 publications · 2.8K citations

    6

    h-index

1–5 of 3.4K

Venues

  1. Applied Soft Computing

    25 publications · 1.2K citations

    21

    h-index

  2. Expert Systems with Applications

    19 publications · 2K citations

    19

    h-index

  3. 16

    h-index

  4. Soft Computing

    12 publications · 914 citations

    12

    h-index

  5. IEEE Transactions on Evolutionary Computation

    11 publications · 3.1K citations

    11

    h-index

1–5