Pareto Front Computation

Pareto Front Computation is a method in multi-objective Markov decision processes that identifies the set of non-dominated policies by simultaneously optimizing multiple conflicting objectives, where no alternative policy can improve one objective without degrading another.

443

Publications

23.6K

Citations

1.3K

Authors

567

Institutions

Publications per year

2017–2026

171

Authors

1.3K

  1. BD
    Benjamin Doerr

    École Polytechnique, France

    8 publications · 298 citations

    8

    h-index

  2. WZ
    Weijie Zheng

    Southern University of Science and Technology, China

    7 publications · 303 citations

    7

    h-index

  3. KD
    Kalyanmoy Deb

    Michigan State University, United States

    7 publications · 2.3K citations

    7

    h-index

  4. ME
    Michael Emmerich

    Leiden University, Netherlands

    6 publications · 209 citations

    6

    h-index

  5. CA
    Carlos A. Coello Coello

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

    6 publications · 763 citations

    6

    h-index

1–5 of 1.3K

Venues

  1. IEEE Transactions on Evolutionary Computation

    11 publications · 3.9K citations

    11

    h-index

  2. arXiv (Cornell University)

    Repository · 10 publications · 193 citations

    10

    h-index

  3. IEEE Transactions on Magnetics

    9 publications · 388 citations

    9

    h-index

  4. Engineering Optimization

    8 publications · 608 citations

    8

    h-index

  5. IEEE Transactions on Cybernetics

    7 publications · 973 citations

    7

    h-index

1–5