Acceleration of Evolutionary Image Filter Design Using Coevolution in Cartesian GP

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Michaela Sikulova and Lukas Sekanina: Acceleration of Evolutionary Image Filter Design Using Coevolution in Cartesian GP. Parallel Problem Solving from Nature, PPSN XII (part 1), Lecture Notes in Computer Science, Vol. 7491, pp. 163-172, Springer, September 1-5 2012.



The aim of this work is to accelerate the task of evolutionary image filter design using coevolution of candidate filters and training vectors subsets. Two coevolutionary methods are implemented and compared for this task in the framework of Cartesian Genetic Programming (CGP). Experimental results show that only 15–20% of original training vectors are needed to find an image filter which provides the same quality of filtering as the best filter evolved using the standard CGP which utilizes the whole training set. Moreover, the median time of evolution was reduced 2.99 times in comparison with the standard CGP.

Extended Abstract


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