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Evolution of image filters on graphics processor units using Cartesian Genetic Programming

Simon Harding
Department Of Computer Science, Memorial University, Newfoundland, Canada, A1B 3X5
IEEE Congress on Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence), p.1921-1928

@conference{harding2008evolution,

   title={Evolution of image filters on graphics processor units using cartesian genetic programming},

   author={Harding, S.},

   booktitle={Evolutionary Computation, 2008. CEC 2008.(IEEE World Congress on Computational Intelligence). IEEE Congress on},

   pages={1921–1928},

   year={2008},

   organization={IEEE}

}

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Graphics processor units are fast, inexpensive parallel computing devices. Recently there has been great interest in harnessing this power for various types of scientific computation, including genetic programming. In previous work, we have shown that using the graphics processor provides dramatic speed improvements over a standard CPU in the context of fitness evaluation. In this work, we use Cartesian Genetic Programming to generate shader programs that implement image filter operations. Using the GPU, we can rapidly apply these programs to each pixel in an image and evaluate the performance of a given filter. We show that we can successfully evolve noise removal filters that produce better image quality than a standard median filter.
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