Evolutionary Computation for Dynamic Optimization Problems
This book provides a compilation on the state-of-the-art and recent advances of evolutionary computation for dynamic optimization problems. The motivation for this book arises from the fact that many real-world optimization problems and engineering system
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490
Shengxiang Yang Xin Yao (Eds.)
Evolutionary Computation for Dynamic Optimization Problems
13
Studies in Computational Intelligence Volume 490
Series Editor J. Kacprzyk, Warsaw, Poland
For further volumes: http://www.springer.com/series/7092
Shengxiang Yang · Xin Yao Editors
Evolutionary Computation for Dynamic Optimization Problems
ABC
Editors Shengxiang Yang School of Computer Science and Informatics De Montfort University The Gateway Leicester LE1 9BH, United Kingdom Xin Yao School of Computer Science University of Birmingham Edgbaston Birmingham B15 2TT, United Kingdom
ISSN 1860-949X ISSN 1860-9503 (electronic) ISBN 978-3-642-38415-8 ISBN 978-3-642-38416-5 (eBook) DOI 10.1007/978-3-642-38416-5 Springer Heidelberg New York Dordrecht London Library of Congress Control Number: 2013938196 c Springer-Verlag Berlin Heidelberg 2013 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. Exempted from this legal reservation are brief excerpts in connection with reviews or scholarly analysis or material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Duplication of this publication or parts thereof is permitted only under the provisions of the Copyright Law of the Publisher’s location, in its current version, and permission for use must always be obtained from Springer. Permissions for use may be obtained through RightsLink at the Copyright Clearance Center. Violations are liable to prosecution under the respective Copyright Law. 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. While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
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Preface
Evolutionary computation (EC) represents a class of optimization methodologies inspired by natural evolution. During the past several decades, evolutionary algorithms (EAs) have been extensively studied by the computer science and artificial intelligence communities. As a class of stochastic optimization techniques, EAs can often outperform classi
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