Symbolic Regression

Symbolic Regression is a regression method that, unlike conventional parametric approaches, automatically discovers the mathematical expression and functional form of a model by searching through symbolic representations of relationships among variables using evolutionary computation techniques.

618

Publications

25.6K

Citations

1.5K

Authors

610

Institutions

Publications per year

2017–2026

384

Authors

1.5K

  1. BX
    Bing Xue

    Victoria University of Wellington, New Zealand

    23 publications · 967 citations

    18

    h-index

  2. MZ
    Mengjie Zhang

    Victoria University of Wellington, New Zealand

    24 publications · 961 citations

    17

    h-index

  3. QC
    Qi Chen

    Victoria University of Wellington, New Zealand

    17 publications · 710 citations

    14

    h-index

  4. GK
    Gabriel Kronberger

    University of Applied Sciences Upper Austria, Austria

    13 publications · 487 citations

    12

    h-index

  5. MK
    Michael Kommenda

    University of Applied Sciences Upper Austria, Austria

    11 publications · 574 citations

    11

    h-index

1–5 of 1.5K

Venues

  1. IEEE Transactions on Evolutionary Computation

    19 publications · 1.8K citations

    17

    h-index

  2. arXiv (Cornell University)

    Repository · 26 publications · 1.1K citations

    16

    h-index

  3. Proceedings of the Genetic and Evolutionary Computation Conference

    Conference · 9 publications · 234 citations

    10

    h-index

  4. Genetic Programming and Evolvable Machines

    9 publications · 884 citations

    9

    h-index

  5. Scientific Reports

    6 publications · 200 citations

    9

    h-index

1–5