Preference-based Fitness Functions

Preference-Based Fitness Functions is a type of fitness function in evolutionary computation that incorporates human or expert preferences to guide the search process by comparing candidate solutions pairwise, thereby distinguishing itself from traditional fitness functions that rely solely on quantitative objective values.

13

Publications

806

Citations

30

Authors

17

Institutions

Publications per year

2017–2026

1

Authors

30

  1. PK
    Pekka Korhonen

    Aalto University, Finland

    2 publications · 385 citations

    2

    h-index

  2. KM
    Kaisa Miettinen

    University of Jyväskylä, Finland

    2 publications · 385 citations

    2

    h-index

  3. LT
    Lothar Thiele

    ETH Zurich, Switzerland

    2 publications · 385 citations

    2

    h-index

  4. JM
    Julián Molina

    Universidad de Málaga, Spain

    2 publications · 385 citations

    2

    h-index

  5. JD
    Joseph D’Ambrosio

    General Motors (United States), United States

    1 publications · 90 citations

    1

    h-index

1–5 of 30

Venues

  1. Evolutionary Computation

    2 publications · 385 citations

    2

    h-index

  2. Evolution

    1 publications · 56 citations

    1

    h-index

  3. Theoretical Computer Science

    1 publications · 11 citations

    1

    h-index

  4. Biology & Philosophy

    1 publications · 10 citations

    1

    h-index

  5. ICCC

    1 publications · 11 citations

    1

    h-index

1–5