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Cartesian Genetic Programming for Image Processing

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Genetic Programming Theory and Practice X


Combining domain knowledge about both imaging processing and machine learning techniques can expand the abilities of Genetic Programming when used for image processing. We successfully demonstrate our new approach on several different problem domains. We show that the approach is fast, scalable and robust. In addition, by virtue of using off-the-shelf image processing libraries we can generate human readable programs that incorporate sophisticated domain knowledge.

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The authors would like to thank Julian Miller for his help in refining this paper.

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Correspondence to Simon Harding .

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Harding, S., Leitner, J., Schmidhuber, J. (2013). Cartesian Genetic Programming for Image Processing. In: Riolo, R., Vladislavleva, E., Ritchie, M., Moore, J. (eds) Genetic Programming Theory and Practice X. Genetic and Evolutionary Computation. Springer, New York, NY.

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  • Print ISBN: 978-1-4614-6845-5

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