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Co-evolution of Fitness Predictors and Deep Neural Networks

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Parallel Processing and Applied Mathematics (PPAM 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10777))

Abstract

Deep neural networks proved to be a very useful and powerful tool with many applications. In order to achieve good learning results, the network architecture has, however, to be carefully designed, which requires a lot of experience and knowledge. Using an evolutionary process to develop new network topologies can facilitate this process. The limiting factor is the speed of evaluation of a single specimen (a single network architecture), which includes learning based on a large dataset. In this paper we propose a new approach which uses subsets of the original training set to approximate the fitness. We describe a co-evolutionary algorithm and discuss its key elements. Finally we draw conclusions from experiments and outline plans for future work.

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Acknowledgement

The research is supported by AGH grant no. 11.11.230.337 and by the PL Grid project with computational resources to carry out experiments.

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Authors and Affiliations

  1. Department of Computer Science, Faculty of Computer Science, Electronics and Telecommunication, AGH, al. Mickiewicza 30, 30-059, Kraków, Poland

    Włodzimierz Funika & Paweł Koperek

Authors
  1. Włodzimierz Funika
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  2. Paweł Koperek
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Corresponding author

Correspondence to Włodzimierz Funika .

Editor information

Editors and Affiliations

  1. Czestochowa University of Technology, Czestochowa, Poland

    Roman Wyrzykowski

  2. University of Tennessee, Knoxville, Tennessee, USA

    Jack Dongarra

  3. University of Southern California, Marina Del Rey, California, USA

    Ewa Deelman

  4. Czestochowa University of Technology, Czestochowa, Poland

    Konrad Karczewski

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Funika, W., Koperek, P. (2018). Co-evolution of Fitness Predictors and Deep Neural Networks. In: Wyrzykowski, R., Dongarra, J., Deelman, E., Karczewski, K. (eds) Parallel Processing and Applied Mathematics. PPAM 2017. Lecture Notes in Computer Science(), vol 10777. Springer, Cham. https://doi.org/10.1007/978-3-319-78024-5_48

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  • DOI: https://doi.org/10.1007/978-3-319-78024-5_48

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-78023-8

  • Online ISBN: 978-3-319-78024-5

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