Cartesian Genetic Programming

Cartesian Genetic Programming is a form of genetic programming that represents programs as directed acyclic graphs arranged in a grid-like Cartesian coordinate system, where nodes correspond to functions and edges represent data flow, differing from traditional tree-based representations by enabling non-linear, modular, and compact encodings.

128

Publications

5.3K

Citations

171

Authors

81

Institutions

Publications per year

2017–2026

41

Variants

Cgp

Authors

171

  1. JF
    Julian F. Miller

    University of York, United Kingdom

    28 publications · 1.4K citations

    20

    h-index

  2. LS
    Lukáš Sekanina

    Brno University of Technology, Czechia

    16 publications · 909 citations

    13

    h-index

  3. GM
    Gul Muhammad Khan

    University of Engineering and Technology Peshawar, Pakistan

    14 publications · 467 citations

    12

    h-index

  4. ZV
    Zdeněk Vašíček

    Brno University of Technology, Czechia

    14 publications · 870 citations

    12

    h-index

  5. SH
    Simon Harding

    University of Liverpool, United Kingdom

    11 publications · 300 citations

    10

    h-index

1–5 of 171

Venues

  1. Genetic Programming and Evolvable Machines

    11 publications · 421 citations

    11

    h-index

  2. IEEE Transactions on Evolutionary Computation

    5 publications · 627 citations

    5

    h-index

  3. Evolutionary Computation

    3 publications · 234 citations

    4

    h-index

  4. Applied Soft Computing

    2 publications · 26 citations

    3

    h-index

  5. IEEE Access

    2 publications · 30 citations

    2

    h-index

1–5