Using aesthetic measures to evolve art

In this paper we investigate and compare three 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. 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). Last, we perform an image analysis using a fixed set of image statistics functions. 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 little flexibility and appreciate only a limited set of images. The images in this paper might only be in color in the electronic version.

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