Coevolving artistic images using OMNIREP

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-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 languageEnglish
Title of host publicationArtificial Intelligence in Music, Sound, Art and Design - 9th International Conference, EvoMUSART 2020, held as part of EvoStar 2020, Proceedings
EditorsJuan Romero, Anikó Ekárt, Tiago Martins, João Correia
PublisherSpringer
Pages165-178
Number of pages14
ISBN (Print)9783030438586
DOIs
StatePublished - 1 Jan 2020
Event9th European Conference on Artificial Intelligence in Music, Sound, Art and Design, EvoMUSART 2020, held as part of EvoStar 2020 - Seville, Spain
Duration: 15 Apr 202017 Apr 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12103 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference9th European Conference on Artificial Intelligence in Music, Sound, Art and Design, EvoMUSART 2020, held as part of EvoStar 2020
Country/TerritorySpain
CitySeville
Period15/04/2017/04/20

Keywords

  • Cooperative coevolution
  • Evolutionary algorithms
  • Evolutionary art
  • Interpretation

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