Coevolving artistic images using OMNIREP

Moshe Sipper; Jason H. Moore; Ryan J. Urbanowicz

doi:10.1007/978-3-030-43859-3_12

Coevolving artistic images using OMNIREP

Moshe Sipper, Jason H. Moore, Ryan J. Urbanowicz

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	Artificial Intelligence in Music, Sound, Art and Design - 9th International Conference, EvoMUSART 2020, held as part of EvoStar 2020, Proceedings
Editors	Juan Romero, Anikó Ekárt, Tiago Martins, João Correia
Publisher	Springer
Pages	165-178
Number of pages	14
ISBN (Print)	9783030438586
DOIs	https://doi.org/10.1007/978-3-030-43859-3_12
State	Published - 1 Jan 2020
Event	9th European Conference on Artificial Intelligence in Music, Sound, Art and Design, EvoMUSART 2020, held as part of EvoStar 2020 - Seville, Spain Duration: 15 Apr 2020 → 17 Apr 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12103 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	9th European Conference on Artificial Intelligence in Music, Sound, Art and Design, EvoMUSART 2020, held as part of EvoStar 2020
Country/Territory	Spain
City	Seville
Period	15/04/20 → 17/04/20

Keywords

Cooperative coevolution
Evolutionary algorithms
Evolutionary art
Interpretation

Access to Document

10.1007/978-3-030-43859-3_12

Cite this

Sipper, M., Moore, J. H., & Urbanowicz, R. J. (2020). Coevolving artistic images using OMNIREP. In J. Romero, A. Ekárt, T. Martins, & J. Correia (Eds.), Artificial Intelligence in Music, Sound, Art and Design - 9th International Conference, EvoMUSART 2020, held as part of EvoStar 2020, Proceedings (pp. 165-178). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12103 LNCS). Springer. https://doi.org/10.1007/978-3-030-43859-3_12

Sipper, Moshe ; Moore, Jason H. ; Urbanowicz, Ryan J. / Coevolving artistic images using OMNIREP. Artificial Intelligence in Music, Sound, Art and Design - 9th International Conference, EvoMUSART 2020, held as part of EvoStar 2020, Proceedings. editor / Juan Romero ; Anikó Ekárt ; Tiago Martins ; João Correia. Springer, 2020. pp. 165-178 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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",

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doi = "10.1007/978-3-030-43859-3_12",

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Sipper, M, Moore, JH & Urbanowicz, RJ 2020, Coevolving artistic images using OMNIREP. in J Romero, A Ekárt, T Martins & J Correia (eds), Artificial Intelligence in Music, Sound, Art and Design - 9th International Conference, EvoMUSART 2020, held as part of EvoStar 2020, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12103 LNCS, Springer, pp. 165-178, 9th European Conference on Artificial Intelligence in Music, Sound, Art and Design, EvoMUSART 2020, held as part of EvoStar 2020, Seville, Spain, 15/04/20. https://doi.org/10.1007/978-3-030-43859-3_12

Coevolving artistic images using OMNIREP. / Sipper, Moshe; Moore, Jason H.; Urbanowicz, Ryan J.

Artificial Intelligence in Music, Sound, Art and Design - 9th International Conference, EvoMUSART 2020, held as part of EvoStar 2020, Proceedings. ed. / Juan Romero; Anikó Ekárt; Tiago Martins; João Correia. Springer, 2020. p. 165-178 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12103 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - 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.

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Sipper M, Moore JH, Urbanowicz RJ. Coevolving artistic images using OMNIREP. In Romero J, Ekárt A, Martins T, Correia J, editors, Artificial Intelligence in Music, Sound, Art and Design - 9th International Conference, EvoMUSART 2020, held as part of EvoStar 2020, Proceedings. Springer. 2020. p. 165-178. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-43859-3_12

Coevolving artistic images using OMNIREP

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