Quality-diversity Search

Quality-Diversity Search is a computational optimization paradigm that, unlike traditional search methods focused solely on maximizing performance, explicitly seeks to generate a diverse collection of high-performing solutions by simultaneously optimizing for both quality and behavioral or structural novelty within a single population or archive.

39

Publications

1.8K

Citations

81

Authors

33

Institutions

Publications per year

2017–2026

30

Variants

Qd OptimizationQd SearchQuality-diversity Optimization

Authors

81

  1. MC
    Matthew C. Fontaine

    University of Southern California, United States

    6 publications · 131 citations

    6

    h-index

  2. SN
    Stefanos Nikolaidis

    University of Southern California, United States

    6 publications · 131 citations

    6

    h-index

  3. KO
    Kenneth O. Stanley

    University of Central Florida, United States

    4 publications · 806 citations

    4

    h-index

  4. AC
    Antoine Cully

    Imperial College London, United Kingdom

    4 publications · 85 citations

    4

    h-index

  5. JT
    Julian Togelius

    New York University, United States

    3 publications · 97 citations

    3

    h-index

1–5 of 81

Venues

  1. Proceedings of the Genetic and Evolutionary Computation Conference

    Conference · 14 publications · 361 citations

    12

    h-index

  2. IEEE Transactions on Evolutionary Computation

    3 publications · 105 citations

    3

    h-index

  3. Journal of the Operational Research Society

    OR · 2 publications · 97 citations

    2

    h-index

  4. Frontiers in Robotics and AI

    2 publications · 495 citations

    2

    h-index

  5. arXiv (Cornell University)

    Repository · 2 publications · 146 citations

    2

    h-index

1–5