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The Problem with Evolutionary Art Is ...

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 6025)

Abstract

Computational evolutionary art has been an active practice for at least 20 years. Given the remarkable advances in that time in other realms of computing, including other forms of evolutionary computing, for many a vague feeling of disappointment surrounds evolutionary art. Aesthetic improvement in evolutionary art has been slow, and typically achieved in ways that are not widely generalizable or extensible. So what is the problem with evolutionary art? And, frankly, why isn’t it better? In this paper I respond to these questions from my point of view as a practicing artist applying both a technical and art theoretical understanding of evolutionary art. First the lack of robust fitness functions is considered with particular attention to the problem of computational aesthetic evaluation. Next the issue of genetic representation is discussed in the context of complexity and emergence. And finally, and perhaps most importantly, the need for art theory around evolutionary and generative art is discussed, and a theory that stands typical evolutionary art on its head is proposed.

Keywords

  • Fitness Function
  • Genetic Representation
  • Evolutionary Computing
  • Aesthetic Judgment
  • Complexification Capacity

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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Galanter, P. (2010). The Problem with Evolutionary Art Is .... In: , et al. Applications of Evolutionary Computation. EvoApplications 2010. Lecture Notes in Computer Science, vol 6025. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12242-2_33

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  • DOI: https://doi.org/10.1007/978-3-642-12242-2_33

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