Acceleration of Evolutionary Image Filter Design Using Coevolution in Cartesian GP

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Reference

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.

DOI

http://dx.doi.org/10.1007/978-3-642-32937-1_17

Abstract

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

Bibtex

Used References

Harding, S.L., Banzhaf, W.: Hardware acceleration for cgp: Graphics processing units. In: Cartesian Genetic Programming, pp. 231–253. Springer (2011)

Hillis, D.W.: Co-evolving parasites improve simulated evolution as an optimization procedure. Physica D: Nonlinear Phenomena 42(1–3), 228–234 (1990) http://dx.doi.org/10.1016/0167-2789(90)90076-2

Koza, J.R., Keane, M.A., Streeter, M.J., Mydlowec, W., Yu, J., Lanza, G.: Genetic Programming IV: Routine Human-Competitive Machine Intelligence. Kluwer Academic Publishers (2003)

Lohn, J., Kraus, W., Haith, G.: Comparing a coevolutionary genetic algorithm for multiobjective optimization. In: Proceedings of the 2002 Congress on Evolutionary Computation, CEC 2002, vol. 2, pp. 1157–1162 (2002)

Miller, J.F.: Cartesian Genetic Programming. Springer (2011)

Pagie, L., Hogeweg, P.: Evolutionary consequences of coevolving targets. Evolutionary Computation 5(4), 401–418 (1997) http://dx.doi.org/10.1162/evco.1997.5.4.401

Rosin, C.D., Bellew, R.K.: New methods for competitive coevolution. Tech. Rep. CS96-491, Department of Computer Science and Engineering, University of California, San Diego (1996)

Schmidt, M.D., Lipson, H.: Coevolution of Fitness Predictors. IEEE Transactions on Evolutionary Computation 12(6), 736–749 (2008) http://dx.doi.org/10.1109/TEVC.2008.919006

Sekanina, L., Harding, L.S., Banzhaf, W., Kowaliw, T.: Image processing and cgp. In: Cartesian Genetic Programming, pp. 181–215. Springer (2011)

Šikulová, M., Sekanina, L.: Coevolution in Cartesian Genetic Programming. In: Moraglio, A., Silva, S., Krawiec, K., Machado, P., Cotta, C. (eds.) EuroGP 2012. LNCS, vol. 7244, pp. 182–193. Springer, Heidelberg (2012) http://dx.doi.org/10.1007/978-3-642-29139-5_16

Vasicek, Z., Sekanina, L.: Hardware accelerator of cartesian genetic programming with multiple fitness units. Computing and Informatics 29(6), 1359–1371 (2010)

Wang, J., Chen, Q.S., Lee, C.H.: Design and implementation of a virtual reconfigurable architecture for different applications of intrinsic evolvable hardware. IET Computers and Digital Techniques 2(5), 386–400 (2008) http://dx.doi.org/10.1049/iet-cdt:20070124


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