- author = "Erik M. Fredericks and Jared M. Moore and Abigail C. Diller",
- title = "{GenerativeGI}: creating generative art with genetic improvement",
- journal = "Automated Software Engineering",
- year = "2024",
- volume = "31",
- pages = "23",
- keywords = "genetic algorithms, genetic programming, Genetic improvement, Grammatical evolution, Generative art, Evolutionary algorithms, Lexicase selection",
- ISSN = "0928-8910",
-
URL = "
https://rdcu.be/emspE",
-
DOI = "
doi:10.1007/s10515-024-00414-3",
- size = "53 pages",
- abstract = "GenerativeGI, an evolutionary computation-based technique for creating generative art by automatically searching through combinations of artistic techniques and their accompanying parameters to produce outputs desirable by the designer. Generative art techniques and their respective parameters are encoded within a grammar that is then the target for genetic improvement. This grammar-based approach, combined with a many-objective evolutionary algorithm, enables the designer to efficiently search through a massive number of possible outputs that reflect their aesthetic preferences. We included a total of 15 generative art techniques and performed three separate empirical evaluations, each of which targets different aesthetic preferences and varying aspects of the search heuristic. Experimental results suggest that GenerativeGI can produce outputs that are significantly more novel than those generated by random or single objective search. Furthermore, GenerativeGI produces individuals with a larger number of relevant techniques used to generate their overall composition.",
-
notes = "Extends \cite{Fredericks:2023:GI}
School of Computing, Grand Valley State University, 1 Campus Dr., Allendale, MI 49401, USA",
