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Exploring non-photorealistic rendering with genetic programming


The field of evolutionary art focuses on using artificial evolution as a means for generating and exploring artistic images and designs. Here, we use evolutionary computation to generate painterly styles of images. A source image is read into the system, and a genetic program is evolved that will re-render the image with non-photorealistic effects. A main contribution of this research is that the colour mixing expression is evolved, which permits a variety of interesting NPR effects to arise. The mixing expression evaluates mathematical properties of the dynamically changing canvas, which results in the evolution of adaptive NPR procedures. Automatic fitness evaluation includes Ralph’s aesthetic model, colour matching, and direct luminosity matching. A few simple techniques for economical brush stroke application on the canvas are supported, which produce different stylistic effects. Using our approach, a number of established, as well as innovative, non-photorealistic painting effects were produced.

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Thanks to Cale Fairchild for his system support, and Beatrice Ombuki-Berman and Sheridan Houghten for their helpful comments. This research is funded by NSERC Discovery Grant 138467.

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Correspondence to Brian J. Ross.

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Baniasadi, M., Ross, B.J. Exploring non-photorealistic rendering with genetic programming. Genet Program Evolvable Mach 16, 211–239 (2015).

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  • Non-photorealistic rendering
  • Genetic programming
  • Evolutionary art.