Multi-objective Evolution for Deep Neural Network Architecture Search
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
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Haiqin Yang · Kitsuchart Pasupa · Andrew Chi-Sing Leung · James T. Kwok · Jonathan H. Chan · Irwin King (Eds.)
Neural Information Processing 27th International Conference, ICONIP 2020 Bangkok, Thailand, November 23–27, 2020 Proceedings, Part III
Lecture Notes in Computer Science Founding Editors Gerhard Goos Karlsruhe Institute of Technology, Karlsruhe, Germany Juris Hartmanis Cornell University, Ithaca, NY, USA
Editorial Board Members Elisa Bertino Purdue University, West Lafayette, IN, USA Wen Gao Peking University, Beijing, China Bernhard Steffen TU Dortmund University, Dortmund, Germany Gerhard Woeginger RWTH Aachen, Aachen, Germany Moti Yung Columbia University, New York, NY, USA
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Haiqin Yang Kitsuchart Pasupa Andrew Chi-Sing Leung James T. Kwok Jonathan H. Chan Irwin King (Eds.) •
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Neural Information Processing 27th International Conference, ICONIP 2020 Bangkok, Thailand, November 23–27, 2020 Proceedings, Part III
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Editors Haiqin Yang Department of AI Ping An Life Shenzhen, China Andrew Chi-Sing Leung City University of Hong Kong Kowloon, Hong Kong Jonathan H. Chan School of Information Technology King Mongkut’s University of Technology Thonburi Bangkok, Thailand
Kitsuchart Pasupa Faculty of Information Technology King Mongkut's Institute of Technology Ladkrabang Bangkok, Thailand James T. Kwok Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong, Hong Kong Irwin King The Chinese University of Hong Kong New Territories, Hong Kong
ISSN 0302-9743 ISSN 1611-3349 (electronic) Lecture Notes in Computer Science ISBN 978-3-030-63835-1 ISBN 978-3-030-63836-8 (eBook) https://doi.org/10.1007/978-3-030-63836-8 LNCS Sublibrary: SL1 – Theoretical Computer Science and General Issues © Springer Nature Switzerland AG 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictiona