Abstract
This paper presents research into the unsupervised evolution of aesthetically pleasing images using measures for symmetry, compositional balance and liveliness. Our evolutionary art system does not use human aesthetic evaluation, but uses measures for symmetry, compositional balance and liveliness as fitness functions. Our symmetry measure calculates the difference in intensity of opposing pixels around one or more axes. Our measure of compositional balance calculates the similarity between two parts of an image using a colour image distance function. Using the latter measure, we are able to evolve images that show a notion of ‘balance’ but are not necessarily symmetrical. Our measure for liveliness uses the entropy of the intensity of the pixels of the image. We evaluated the effect of these aesthetic measures by performing a number of experiments in which each aesthetic measure was used as a fitness function. We combined our measure for symmetry with existing aesthetic measures using a multi-objective evolutionary algorithm (NSGA-II).
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den Heijer, E. (2015). Evolving Symmetric and Balanced Art. In: Madani, K., Correia, A., Rosa, A., Filipe, J. (eds) Computational Intelligence. IJCCI 2012. Studies in Computational Intelligence, vol 577. Springer, Cham. https://doi.org/10.1007/978-3-319-11271-8_3
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DOI: https://doi.org/10.1007/978-3-319-11271-8_3
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