Coevolving Artistic Images Using OMNIREP
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909078298558464 |
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| author | Sipper, Moshe Moore, Jason H. Urbanowicz, Ryan J. |
| author_facet | Sipper, Moshe Moore, Jason H. Urbanowicz, Ryan J. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_11167 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Coevolving Artistic Images Using OMNIREP Sipper, Moshe Moore, Jason H. Urbanowicz, Ryan J. Neural and Evolutionary Computing 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. |
| title | Coevolving Artistic Images Using OMNIREP |
| topic | Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2401.11167 |