- author = "Edward Easton and Aniko Ekart and Ulysses Bernardet",
- title = "Axial Generation: A Concretism-Inspired Method for Synthesizing Highly Varied Artworks",
- booktitle = "EvoMUSART 2021, Artificial Intelligence in Music, Sound, Art and Design",
- year = "2021",
- month = "7-9 " # apr,
- editor = "J. Romero and T. Martins and N. Rodriguez-Fernandez",
- series = "LNCS",
- volume = "12693",
- publisher = "Springer Verlag",
- address = "Seville, Spain",
- pages = "115--130",
- organisation = "EvoStar, Species",
- keywords = "genetic algorithms, genetic programming, Evolutionary computation, 2D and 3D art generation, Concretism",
- isbn13 = "978-3-030-72913-4",
-
URL = "
https://research.aston.ac.uk/en/publications/axial-generation-a-concretism-inspired-method-for-synthesizing-hi",
-
DOI = "
doi:10.1007/978-3-030-72914-1_8",
- size = "16 pages",
- abstract = "Automated computer generation of aesthetically pleasing artwork has been the subject of research for several decades. The unsolved problem of interest is how to automatically please any audience without too much involvement of the said audience in the process of creation. Two dimensional pictures have received a lot of attention, however 3D artwork has remained relatively unexplored. This paper introduces the Axial Generation Process (AGP), a versatile generation algorithm that can be employed to create both 2D and 3D items within the Concretism art style. The evaluation of items generated using the process using a set of formal aesthetic measures, shows the process to be capable of generating visually varied items which generally exhibit a diverse range of values across the measures used, in both two and three dimensions.",
-
notes = "Computer Science, Aston University, Birmingham,
UK
http://www.evostar.org/2020/ EvoMusArt2020 held in conjunction with EvoApplications2020, EuroGP'2020 and EvoCOP2020",
