Multi-objective Evolutionary Algorithms

Multi-Objective Evolutionary Algorithms is a class of population-based stochastic search and optimization algorithms that extend classical evolutionary algorithms by simultaneously optimizing multiple conflicting objectives through mechanisms such as Pareto-based dominance ranking and diversity preservation to approximate the Pareto optimal set.

5K

Publications

419.2K

Citations

9.9K

Authors

2.4K

Institutions

Publications per year

2017–2026

2.1K

Variants

Moeas

Authors

9.9K

  1. KD
    Kalyanmoy Deb

    Michigan State University, United States

    116 publications · 85.5K citations

    56

    h-index

  2. YJ
    Yaochu Jin

    University of Surrey, United Kingdom

    87 publications · 16.1K citations

    52

    h-index

  3. CA
    Carlos A. Coello Coello

    Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Mexico

    113 publications · 17.7K citations

    48

    h-index

  4. QZ
    Qingfu Zhang

    City University of Hong Kong, Hong Kong SAR China

    68 publications · 21.7K citations

    44

    h-index

  5. HI
    Hisao Ishibuchi

    Osaka Prefecture University, Japan

    83 publications · 7.7K citations

    40

    h-index

1–5 of 9.9K

Venues

  1. IEEE Transactions on Evolutionary Computation

    269 publications · 135K citations

    121

    h-index

  2. IEEE Transactions on Cybernetics

    92 publications · 11.3K citations

    59

    h-index

  3. Applied Soft Computing

    189 publications · 9.2K citations

    51

    h-index

  4. Expert Systems with Applications

    121 publications · 8.4K citations

    50

    h-index

  5. Information Sciences

    155 publications · 9K citations

    50

    h-index

1–5