@inproceedings{8b30887779e94bd9a0923bd5173fb494,
title = "Coevolving artistic images using OMNIREP",
abstract = "We have recently developed OMNIREP, a coevolutionary algorithm to discover both a representation and an interpreter that solve a particular problem of interest. Herein, we demonstrate that the OMNIREP framework can be successfully applied within the field of evolutionary art. Specifically, we coevolve representations that encode image position, alongside interpreters that transform these positions into one of three pre-defined shapes (chunks, polygons, or circles) of varying size, shape, and color. We showcase a sampling of the unique image variations produced by this approach.",
keywords = "Cooperative coevolution, Evolutionary algorithms, Evolutionary art, Interpretation",
author = "Moshe Sipper and Moore, {Jason H.} and Urbanowicz, {Ryan J.}",
note = "Funding Information: This work was supported by National Institutes of Health (USA) grants LM010098, LM012601, AI116794. Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2020.; 9th European Conference on Artificial Intelligence in Music, Sound, Art and Design, EvoMUSART 2020, held as part of EvoStar 2020 ; Conference date: 15-04-2020 Through 17-04-2020",
year = "2020",
month = apr,
day = "20",
doi = "10.1007/978-3-030-43859-3_12",
language = "English",
isbn = "978-3-030-43858-6",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "165--178",
editor = "Juan Romero and Anik{\'o} Ek{\'a}rt and Tiago Martins and Jo{\~a}o Correia",
booktitle = "Artificial Intelligence in Music, Sound, Art and Design - 9th International Conference, EvoMUSART 2020, held as part of EvoStar 2020, Proceedings",
address = "Germany",
}