Genetic Programming For Design

Genetic Programming for Design is a computational methodology within evolutionary design that employs principles of genetic algorithms—such as mutation, crossover, and selection—to autonomously generate and optimize design solutions through iterative improvement of encoded design representations.

347

Publications

15.5K

Citations

817

Authors

360

Institutions

Publications per year

2017–2026

73

Authors

817

  1. MA
    Martin A. Keane

    Stanford University, United States

    13 publications · 746 citations

    11

    h-index

  2. DJ
    Domagoj Jakobović

    University of Zagreb, Croatia

    10 publications · 320 citations

    10

    h-index

  3. JR
    John R. Koza

    Stanford University, United States

    9 publications · 633 citations

    9

    h-index

  4. ED
    Erik D. Goodman

    Michigan State University, United States

    9 publications · 263 citations

    9

    h-index

  5. JK
    J.R. Koza

    Stanford University, United States

    9 publications · 905 citations

    9

    h-index

1–5 of 817

Venues

  1. Applied Soft Computing

    12 publications · 576 citations

    12

    h-index

  2. IEEE Transactions on Evolutionary Computation

    12 publications · 1.5K citations

    11

    h-index

  3. Genetic Programming and Evolvable Machines

    11 publications · 518 citations

    11

    h-index

  4. Expert Systems with Applications

    11 publications · 744 citations

    11

    h-index

  5. 5

    h-index

1–5