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Adaptive Critics for Evolutionary Artists

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNCS,volume 3005)

Abstract

We focus on the development of artificial art critics. These systems analyze artworks, extracting relevant features, and produce an evaluation of the perceived pieces. The ability to perform aesthetic judgments is a desirable characteristic in an evolutionary artificial artist. As such, the inclusion of artificial art critics in these systems may improve their artistic abilities. We propose artificial art critics for the domains of music and visual arts, presenting a comprehensive set of experiments in author identification tasks. The experimental results show the viability and potential of our approach.

Keywords

  • Feature Extractor
  • Aesthetic Judgment
  • Musical Score
  • Zipf Distribution
  • Evolutionary Artist

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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  • DOI: 10.1007/978-3-540-24653-4_45
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© 2004 Springer-Verlag Berlin Heidelberg

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Machado, P., Romero, J., Santos, M.L., Cardoso, A., Manaris, B. (2004). Adaptive Critics for Evolutionary Artists. In: , et al. Applications of Evolutionary Computing. EvoWorkshops 2004. Lecture Notes in Computer Science, vol 3005. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24653-4_45

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  • DOI: https://doi.org/10.1007/978-3-540-24653-4_45

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-21378-9

  • Online ISBN: 978-3-540-24653-4

  • eBook Packages: Springer Book Archive