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A System for Evolving Art Using Supervised Learning and Aesthetic Analogies

  • Aidan BreenEmail author
  • Colm O’Riordan
Chapter
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Part of the Studies in Computational Intelligence book series (SCI, volume 792)

Abstract

Aesthetic experience is an important aspect of creativity and our perception of the world around us. Analogy is a tool we use as part of the creative process to translate our perceptions into creative works of art. In this paper we present our research on the development of an artificially intelligent system for the creation of art in the form of real-time visual displays to accompany a given music piece. The presented system achieves this by using Grammatical Evolution, a form of Evolutionary Computation, to evolve Mapping Expressions. These expressions form part of a conceptual structure, described herein, which allows aesthetic data to be gathered and analogies to be made between music and visuals. The system then uses the evolved mapping expressions to generate visuals in real-time, given some musical input. The output is a novel visual display, similar to concert or stage lighting which is reactive to input from a performer.

Keywords

Genetic algorithms Evolutionary art and design Genetic programming Hybrid systems Computational analogy Aesthetics 

Notes

Acknowledgements

The work presented in this paper was kindly funded by the Hardiman Scholarship, National University of Ireland, Galway. The authors would also like to thank the staff and students of the Computational Intelligence Research Group (CIRG), Department of Information Technology, and the School of Mathematics, Statistics and Applied Mathematics, NUIG for their guidance and support throughout the development of this work.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.National University of IrelandGalwayIreland

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