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Acceleration of Evolutionary Image Filter Design Using Coevolution in Cartesian GP

  • Michaela Sikulova
  • Lukas Sekanina
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7491)

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.

Keywords

Execution Time Graphic Processing Unit Training Image Evolutionary Design Training Vector 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Michaela Sikulova
    • 1
  • Lukas Sekanina
    • 1
  1. 1.Faculty of Information Technology, IT4Innovations Centre of ExcellenceBrno University of TechnologyBrnoCzech Republic

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