User-guided Evolutionary Search

User-Guided Evolutionary Search is an evolutionary search framework in which human users iteratively evaluate and select candidate solutions to guide the optimization process, distinguishing it from fully automated evolutionary algorithms by integrating human judgment as a central mechanism for fitness assessment and search direction.

25

Publications

683

Citations

56

Authors

36

Institutions

Publications per year

2017–2026

2

Authors

56

  1. JB
    Josh Bongard

    University of Vermont, United States

    3 publications · 54 citations

    3

    h-index

  2. GS
    Gregory S. Hornby

    Ames Research Center, United States

    3 publications · 54 citations

    3

    h-index

  3. KO
    Kenneth O. Stanley

    University of Central Florida, United States

    2 publications · 42 citations

    2

    h-index

  4. BG
    Brian G. Woolley

    U.S. Air Force Institute of Technology, United States

    2 publications · 42 citations

    2

    h-index

  5. GD
    Giorgio Davanzo

    University of Trieste, Italy

    1 publications · 46 citations

    1

    h-index

1–5 of 56

Venues

  1. IEEE Transactions on Evolutionary Computation

    2 publications · 101 citations

    2

    h-index

  2. UCL Discovery (University College London)

    Repository · 1 publications · 10 citations

    1

    h-index

  3. New Generation Computing

    1 publications · 18 citations

    1

    h-index

  4. 1

    h-index

  5. ePrints Soton (University of Southampton)

    Repository · 1 publications · 14 citations

    1

    h-index

1–5