Proceedings of ELM-2014_ Algorithms and Theories (vol. 1) [Cao, Mao, Cambria, Man & Toh 2014-12-04].pdf

(15786 KB) Pobierz
Proceedings in Adaptation, Learning and Optimization 3
Jiuwen Cao · Kezhi Mao
Erik Cambria · Zhihong Man
Kar-Ann Toh
Editors
Proceedings of ELM-2014
Volume 1
Algorithms and Theories
Proceedings in Adaptation, Learning
and Optimization
Volume 3
Series editors
Yew Soon Ong, Nanyang Technological University, Singapore
e-mail: asysong@ntu.edu.sg
Meng-Hiot Lim, Nanyang Technological University, Singapore
e-mail: emhlim@ntu.edu.sg
Board of editors
Hussain Abbas, University of New South Wales, Australia
Giovanni Acampora, Nottingham Trent University, Nottingham, UK
Enrique Alba, University of Málaga, Málaga, Spain
Jonathan Chan, King Mongkut’s University of Technology Thonburi (KMUTT),
Bangkok, Thailand
Sung-Bae Cho, Yonsei University, Seoul, Korea
Hisao Ishibuchi, Osaka Prefecture University, Osaka, Japan
Wilfried Jakob, Institute for Applied Computer Science (IAI), Germany
Jose A. Lozano, University of the Basque Country UPV/EHU, Spain
Zhang Mengjie, Victoria University of Wellington, Wellington, New Zealand
Jim Smith, University of the West of England, Bristol, UK
Kay-Chen Tan, National University of Singapore, Singapore
Ke Tang, School of Computer Science and Technology, China
Chuang-Kang Ting, National Chung Cheng University, Taiwan
Donald C. Wunsch, Missouri University of Science & Technology, USA
Jin Yaochu, University of Surrey, UK
About this Series
The role of adaptation, learning and optimization are becoming increasingly essential
and intertwined. The capability of a system to adapt either through modification of its
physiological structure or via some revalidation process of internal mechanisms that
directly dictate the response or behavior is crucial in many real world applications. Op-
timization lies at the heart of most machine learning approaches while learning and
optimization are two primary means to effect adaptation in various forms. They usually
involve computational processes incorporated within the system that trigger parametric
updating and knowledge or model enhancement, giving rise to progressive improve-
ment. This book series serves as a channel to consolidate work related to topics linked
to adaptation, learning and optimization in systems and structures. Topics covered under
this series include:
•
complex adaptive systems including evolutionary computation, memetic comput-
ing, swarm intelligence, neural networks, fuzzy systems, tabu search, simulated
annealing, etc.
•
machine learning, data mining & mathematical programming
•
hybridization of techniques that span across artificial intelligence and computa-
tional intelligence for synergistic alliance of strategies for problem-solving
•
aspects of adaptation in robotics
•
agent-based computing
•
autonomic/pervasive computing
•
dynamic optimization/learning in noisy and uncertain environment
•
systemic alliance of stochastic and conventional search techniques
•
all aspects of adaptations in man-machine systems.
This book series bridges the dichotomy of modern and conventional mathematical and
heuristic/meta-heuristics approaches to bring about effective adaptation, learning and
optimization. It propels the maxim that the old and the new can come together and
be combined synergistically to scale new heights in problem-solving. To reach such a
level, numerous research issues will emerge and researchers will find the book series a
convenient medium to track the progresses made.
More information about this series at http://www.springer.com/series/13543
Jiuwen Cao
·
Kezhi Mao
Erik Cambria
·
Zhihong Man
Kar-Ann Toh
Editors
Proceedings of ELM-2014
Volume 1
Algorithms and Theories
ABC
Editors
Jiuwen Cao
Institute of Information and Control
Hangzhou Dianzi University
Zhejiang
China
Kezhi Mao
School of Electrical and Electronic
Engineering
Nanyang Technological University
Singapore
Singapore
Erik Cambria
School of Computer Engineering
Nanyang Technological University
Singapore
Singapore
Zhihong Man
Faculty of Engineering and Industrial
Sciences
Swinburne University of Technology
Hawthorn Victoria
Australia
Kar-Ann Toh
School of Electrical and Electronic
Engineering
Yonsei University
Seoul
Korea, Republic of (South Korea)
ISSN 2363-6084
ISSN 2363-6092 (electronic)
Proceedings in Adaptation, Learning and Optimization
ISBN 978-3-319-14062-9
ISBN 978-3-319-14063-6 (eBook)
DOI 10.1007/978-3-319-14063-6
Library of Congress Control Number: 2014957491
Springer Cham Heidelberg New York Dordrecht London
c
Springer International Publishing Switzerland 2015
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, broad-
casting, 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, express or implied, with respect to the material contained herein or for any errors or
omissions that may have been made.
Printed on acid-free paper
Springer International Publishing AG Switzerland is part of Springer Science+Business Media
(www.springer.com)

Plik z chomika:

Inne pliki z tego folderu:

Inne foldery tego chomika:

Zgłoś jeśli naruszono regulamin