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Comparing Aesthetic Measures for Evolutionary Art

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

Abstract

In this paper we investigate and compare four aesthetic measures within the context of evolutionary art. We evolve visual art with an unsupervised evolutionary art system using genetic programming and an aesthetic measure as the fitness function. We perform multiple experiments with different aesthetic measures and examine their influence on the evolved images. To this end we store the 5 fittest individuals of each run and hand-pick the best 9 images after finishing the whole series. This way we create a portfolio of evolved art for each aesthetic measure for visual inspection. Additionally, we perform a cross-evaluation by calculating the aesthetic value of images evolved by measure i according to measure j. This way we investigate the flexiblity of each aesthetic measure (i.e., whether the aesthetic measure appreciates different types of images). The results show that aesthetic measures have a rather clear ”style” and that these styles can be very different. Furthermore we find that some aesthetic measures show very little flexibility and appreciate only a limited set of images.

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  • DOI: 10.1007/978-3-642-12242-2_32
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den Heijer, E., Eiben, A.E. (2010). Comparing Aesthetic Measures for Evolutionary Art. 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_32

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  • DOI: https://doi.org/10.1007/978-3-642-12242-2_32

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-12241-5

  • Online ISBN: 978-3-642-12242-2

  • eBook Packages: Computer ScienceComputer Science (R0)