Genetic Algorithms

Genetic Algorithms is a computational optimization method within evolutionary algorithms that employs mechanisms inspired by biological evolution, such as selection, crossover, and mutation, to iteratively generate solutions to problems through successive generations of a population of candidate solutions.

46.3K

Publications

2.8M

Citations

81.5K

Authors

8.6K

Institutions

Publications per year

2017–2026

12.4K

Authors

81.5K

  1. KD
    Kalyanmoy Deb

    Michigan State University, United States

    161 publications · 99.5K citations

    74

    h-index

  2. DE
    David E. Goldberg

    University of Illinois Urbana-Champaign, United States

    129 publications · 25.5K citations

    66

    h-index

  3. FH
    Francisco Herrera

    Universidad de Granada, Spain

    118 publications · 22.1K citations

    66

    h-index

  4. MG
    Mitsuo Gen

    Ashikaga University, Japan

    145 publications · 10.7K citations

    54

    h-index

  5. XY
    Xin Yao

    University of Birmingham, United Kingdom

    124 publications · 16.3K citations

    51

    h-index

1–5 of 81.5K

Venues

  1. IEEE Transactions on Evolutionary Computation

    557 publications · 162.3K citations

    151

    h-index

  2. Expert Systems with Applications

    799 publications · 59.1K citations

    121

    h-index

  3. IEEE Transactions on Power Systems

    202 publications · 37.7K citations

    113

    h-index

  4. Applied Soft Computing

    966 publications · 64.2K citations

    113

    h-index

  5. Information Sciences

    492 publications · 44K citations

    101

    h-index

1–5