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Multi-objective Evolution for Deep Neural Network Architecture Search

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Neural Information Processing (ICONIP 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12534))

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Abstract

In this paper, we propose a multi-objective evolutionary algorithm for automatic deep neural architecture search. The algorithm optimizes the performance of the model together with the number of network parameters. This allows exploring architectures that are both successful and compact. We test the proposed solution on several image classification data sets including MNIST, fashionMNIST and CIFAR-10, and we consider deep architectures including convolutional and fully connected networks. The effects of using two different versions of multi-objective selections are also examined in the paper. Our approach outperforms both the considered baseline architectures and the standard genetic algorithm used in our previous work.

This work was partially supported by the Czech Science Foundation project no. 18-23827S and institutional support of the Institute of Computer Science RVO 67985807.

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Author information

Authors and Affiliations

  1. The Czech Academy of Sciences, Institute of Computer Science, Pod Vodárenskou Věží 2, 182 07, Prague 8, Czech Republic

    Petra Vidnerová & Roman Neruda

Authors
  1. Petra Vidnerová
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  2. Roman Neruda
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Corresponding author

Correspondence to Petra Vidnerová .

Editor information

Editors and Affiliations

  1. Department of AI, Ping An Life, Shenzhen, China

    Haiqin Yang

  2. Faculty of Information Technology, King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand

    Kitsuchart Pasupa

  3. City University of Hong Kong, Kowloon, Hong Kong

    Andrew Chi-Sing Leung

  4. Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, Hong Kong

    James T. Kwok

  5. School of Information Technology, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand

    Jonathan H. Chan

  6. The Chinese University of Hong Kong, New Territories, Hong Kong

    Irwin King

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Vidnerová, P., Neruda, R. (2020). Multi-objective Evolution for Deep Neural Network Architecture Search. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Lecture Notes in Computer Science(), vol 12534. Springer, Cham. https://doi.org/10.1007/978-3-030-63836-8_23

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  • DOI: https://doi.org/10.1007/978-3-030-63836-8_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-63835-1

  • Online ISBN: 978-3-030-63836-8

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