Selection Window Optimization

Selection Window Optimization is a methodological framework within the field of evolutionary computation that specifically enables the systematic adjustment of temporal or generational intervals during which selection pressure is applied, thereby differentiating it from broader selection strategies through its focus on optimizing the timing and duration of selective phases to enhance evolutionary dynamics.

22

Publications

1.4K

Citations

91

Authors

47

Institutions

Publications per year

2017–2026

5

Authors

91

  1. YD
    Yonggui Dong

    Tsinghua University, China

    1 publications · 28 citations

    1

    h-index

  2. SD
    Silvio D. Rodríguez

    Universidad de Buenos Aires, Argentina

    1 publications · 20 citations

    1

    h-index

  3. PI
    Pyeongchan Ihm

    University of Colorado Boulder, United States

    1 publications · 68 citations

    1

    h-index

  4. YJ
    Yong Jie See

    Sunway University, Malaysia

    1 publications · 14 citations

    1

    h-index

  5. LP
    Lyool Park

    Dong-A University, South Korea

    1 publications · 68 citations

    1

    h-index

1–5 of 91

Venues

  1. Journal on Multimodal User Interfaces

    1 publications · 19 citations

    1

    h-index

  2. Energy Policy

    1 publications · 68 citations

    1

    h-index

  3. IEEE Transactions on Nuclear Science

    1 publications · 11 citations

    1

    h-index

  4. Radiation Physics and Chemistry

    1 publications · 14 citations

    1

    h-index

  5. Physics in Medicine and Biology

    1 publications · 35 citations

    1

    h-index

1–5