Investigating Deep Recurrent Connections and Recurrent Memory Cells Using Neuro-Evolution
Neural architecture search poses one of the most difficult problems for statistical learning, given the incredibly vast architectural search space. This problem is further compounded for recurrent neural networks (RNNs), where every node in an architectur
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Hitoshi Iba Nasimul Noman Editors
Deep Neural Evolution Deep Learning with Evolutionary Computation
Natural Computing Series Series Editors: Thomas B¨ack Lila Kari
Natural Computing is one of the most exciting developments in computer science, and there is a growing consensus that it will become a major field in this century. This series includes monographs, textbooks, and state-of-the-art collections covering the whole spectrum of Natural Computing and ranging from theory to applications.
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Hitoshi Iba • Nasimul Noman Editors
Deep Neural Evolution Deep Learning with Evolutionary Computation
Editors Hitoshi Iba Graduate School of Information Science and Technology The University of Tokyo Tokyo, Japan
Nasimul Noman School of Electrical Engineering and Computing The University of Newcastle Callaghan, NSW, Australia
ISSN 1619-7127 Natural Computing Series ISBN 978-981-15-3684-7 ISBN 978-981-15-3685-4 (eBook) https://doi.org/10.1007/978-981-15-3685-4 © Springer Nature Singapore Pte Ltd. 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 jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore
Preface
The scientific interest in designing synergic systems using neural networks (NN) and evolutionary computation (EC) together, which began long ago, created the field of neuroevolution. The ultimate goal of this subfield of artificial intelligence is to create optimally designed neural networks, capable of exhibiting intelligent behavior, with no or minimum human intervention. Towards this aim, over the last decades, the concepts of neuroevolution have been effectively used with a wide range of neural network models and evolutionary algo
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