Generative Design Algorithms

Generative Design Algorithms is a computational methodology within the field of design automation that systematically synthesizes optimal design solutions through iterative simulation-driven processes, distinguished by its integration of performance criteria, material constraints, and morphological evolution to automate the exploration of high-performing topological configurations.

205

Publications

10.5K

Citations

637

Authors

233

Institutions

Publications per year

2017–2026

147

Authors

637

  1. LC
    Luisa Caldas

    University of California, Berkeley, United States

    6 publications · 376 citations

    6

    h-index

  2. KS
    Kristina Shea

    ETH Zurich, Switzerland

    4 publications · 98 citations

    4

    h-index

  3. CC
    Carolyn Conner Seepersad

    The University of Texas at Austin, United States

    4 publications · 72 citations

    4

    h-index

  4. FA
    Faez Ahmed

    Massachusetts Institute of Technology, United States

    3 publications · 132 citations

    3

    h-index

  5. KY
    Kentaro Yaji

    The University of Osaka, Japan

    3 publications · 132 citations

    3

    h-index

1–5 of 637

Venues

  1. Automation in Construction

    15 publications · 970 citations

    13

    h-index

  2. Journal of Mechanical Design

    10 publications · 700 citations

    10

    h-index

  3. ACADIA quarterly

    7 publications · 128 citations

    7

    h-index

  4. International Journal of Architectural Computing

    IJAC · 5 publications · 98 citations

    5

    h-index

  5. Structural and Multidisciplinary Optimization

    4 publications · 195 citations

    4

    h-index

1–5