Generative Aesthetic Design

Generative Aesthetic Design is a computational methodology within computational aesthetics that autonomously produces or evolves aesthetic artifacts through rule-based, algorithmic, or learning-driven processes, distinguishing itself by integrating generative mechanisms with formal aesthetic evaluation to create novel, perceptually meaningful designs.

10

Publications

564

Citations

40

Authors

18

Institutions

Publications per year

2017–2026

8

Authors

40

  1. AB
    Alex Burnap

    University of Michigan, United States

    2 publications · 108 citations

    2

    h-index

  2. JR
    John R. Hauser

    North Carolina State University, United States

    2 publications · 108 citations

    2

    h-index

  3. AT
    Artem Timoshenko

    Massachusetts Institute of Technology, United States

    2 publications · 108 citations

    2

    h-index

  4. CY
    Chaoxiang Yang

    East China University of Science and Technology, China

    1 publications · 28 citations

    1

    h-index

  5. SN
    Stephen N. Smyth

    Massachusetts Institute of Technology, United States

    1 publications · 43 citations

    1

    h-index

1–5 of 40

Venues

  1. Connection Science

    1 publications · 64 citations

    1

    h-index

  2. Proceedings of DRS

    1 publications · 120 citations

    1

    h-index

  3. Advanced Engineering Informatics

    1 publications · 28 citations

    1

    h-index

  4. Design Studies

    1 publications · 88 citations

    1

    h-index

  5. Marketing Science

    1 publications · 86 citations

    1

    h-index

1–5