Concepedia

Generative Art Performance

Generative Art Performance is a computational methodology that specifies how generative algorithms produce artistic outputs through dynamic, often autonomous, decision-making processes that integrate aesthetic criteria, procedural rules, and performance metrics to evaluate and refine expressive outcomes.

3

Publications

59

Citations

4

Authors

3

Institutions

Publications per year

2017–2026

0

Parents

Publications

3

1–4 of 3

Authors

4

  1. BC
    Ben Caplan

    The Ohio State University, United States

    1 publications · 20 citations

    1

    h-index

  2. EE
    Ernest Edmonds

    University of Technology Sydney, Australia

    1 publications · 23 citations

    1

    h-index

  3. CM
    Carl Matheson

    University of Manitoba, Canada

    1 publications · 20 citations

    1

    h-index

  4. JA
    John A. Biles

    Rochester Institute of Technology, United States

    1 publications · 16 citations

    1

    h-index

  5. AP
    Antonio Pošćić

    1 publications · 10 citations

    0

    h-index

1–5 of 4

Institutions

3

  1. University of Manitoba

    Winnipeg, Canada

    1 h-index

    1 publications

    20 citations

  2. The Ohio State University

    Columbus, United States

    1 h-index

    1 publications

    20 citations

  3. Rochester Institute of Technology

    Rochester, United States

    1 h-index

    1 publications

    16 citations

1–3 of 3

Venues

  1. Canadian Journal of Philosophy

    CJP · 1 publications · 20 citations

    1

    h-index

  2. Digital Creativity

    1 publications · 23 citations

    1

    h-index

1–3