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Evolving Node Transfer Functions in Artificial Neural Networks for Handwritten Digits Recognition

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Computer Vision and Graphics (ICCVG 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9972))

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Abstract

Feed-forward Artificial Neural Networks are popular choices among scientists and engineers for modeling complex real-world problems. One of the latest research areas in this field is evolving Artificial Neural Networks: NeuroEvolution. In this paper we investigate the effect of evolving a node transfer function and its parameters along with the evolution of connection weights in Evolutionary Artificial Neural Networks for the problem of handwritten digits recognition. The results are promising when compared with the traditional approach of homogeneous Artificial Neural Network with predefined transfer function.

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Authors and Affiliations

  1. Institute of Computer Science, Warsaw University of Technology, Nowowiejska 15/19, 00-665, Warsaw, Poland

    Dmytro Vodianyk & Przemysław Rokita

Authors
  1. Dmytro Vodianyk
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  2. Przemysław Rokita
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Corresponding author

Correspondence to Dmytro Vodianyk .

Editor information

Editors and Affiliations

  1. Faculty of Applied Informatics and Math, Warsaw University of Life Sciences , Warsaw, Poland

    Leszek J. Chmielewski

  2. Computer Sci and Software Engineering, University of Western Australia , Perth, Australia

    Amitava Datta

  3. Faculty of Applied Informatics and Math, Warsaw University of Life Sciences SGGW , Warsaw, Poland

    Ryszard Kozera

  4. Institute of Computer Science, Silesian University of Technology , Gliwice, Poland

    Konrad Wojciechowski

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Vodianyk, D., Rokita, P. (2016). Evolving Node Transfer Functions in Artificial Neural Networks for Handwritten Digits Recognition. In: Chmielewski, L., Datta, A., Kozera, R., Wojciechowski, K. (eds) Computer Vision and Graphics. ICCVG 2016. Lecture Notes in Computer Science(), vol 9972. Springer, Cham. https://doi.org/10.1007/978-3-319-46418-3_54

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  • DOI: https://doi.org/10.1007/978-3-319-46418-3_54

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46417-6

  • Online ISBN: 978-3-319-46418-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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