- author = "Edward Easton and Ulysses Bernardet and Aniko Ekart",
- title = "Modelling Individual Aesthetic Preferences of 3D Sculptures",
- booktitle = "13th International Conference on Artificial Intelligence in Music, Sound, Art and Design, EvoMusArt 2024",
- year = "2024",
- editor = "Colin Johnson and Sergio M. Rebelo and Iria Santos",
- series = "LNCS",
- volume = "14633",
- publisher = "Springer",
- address = "Aberystwyth",
- month = "3-5 " # apr,
- pages = "130--145",
- organisation = "EvoStar, Species",
- keywords = "genetic algorithms, genetic programming, Aesthetic judgement, 3D Art Generation, Aesthetic modelling",
- isbn13 = "978-3-031-56991-3",
-
URL = "
https://rdcu.be/dD0Gt",
-
DOI = "
doi:10.1007/978-3-031-56992-0_9",
- size = "16 pages",
- abstract = "Aesthetic preference is a complex puzzle with many subjective aspects. This subjectivity makes it incredibly difficult to computationally model aesthetic preference for an individual. Despite this complexity, individual aesthetic preference is an important part of life, impacting a multitude of aspects, including romantic and platonic relationships, decoration, product choices and artwork. Models of aesthetic preference form the basis of automated and semi-automated Evo-Art systems. These range from looking at individual aspects to more complex models considering multiple, different criteria. Effectively modelling aesthetic preference greatly increases the potential impact of these systems. This paper presents a flexible computational model of aesthetic preference, primarily focusing on generating 3D sculptures. Through demonstrating the model using several examples, it is shown that the model is flexible enough to identify and respond to individual aesthetic preferences,...",
-
notes = "Aston Centre for AI Research and Application (ACAIRA),
Aston University, Birmingham,
UK
http://www.evostar.org/2024/ EvoMusArt2024 held in conjunction with EuroGP'2024, EvoCOP2024 and EvoApplications2024",
