Evolutionary Constrained Optimization
This book makes available a self-contained collection of modern research addressing the general constrained optimization problems using evolutionary algorithms. Broadly the topics covered include constraint handling for single and multi-objective opt
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Rituparna Datta Kalyanmoy Deb Editors
Evolutionary Constrained Optimization
Infosys Science Foundation Series Applied Sciences and Engineering
More information about this series at http://www.springer.com/series/13554
Rituparna Datta Kalyanmoy Deb •
Editors
Evolutionary Constrained Optimization
123
Editors Rituparna Datta Department of Electrical Engineering Korea Advanced Institute of Science and Technology Daejeon Republic of Korea
Kalyanmoy Deb Electrical and Computer Engineering Michigan State University East Lansing, MI USA
ISSN 2363-6149 ISSN 2363-6157 (electronic) Infosys Science Foundation Series ISSN 2363-4995 ISSN 2363-5002 (electronic) Applied Sciences and Engineering ISBN 978-81-322-2183-8 ISBN 978-81-322-2184-5 (eBook) DOI 10.1007/978-81-322-2184-5 Library of Congress Control Number: 2014957133 Springer New Delhi Heidelberg New York Dordrecht London © Springer India 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, 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, 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 (India) Pvt. Ltd. is part of Springer Science+Business Media (www.springer.com)
To my parents, Ranjit Kumar Datta (father) and Khela Datta (mother). Rituparna Datta To Sadhan Chandra Deb (“Baro Mama”, Eldest Uncle) whose inspiration has always shown me the way. Kalyanmoy Deb
Preface
Optimization is an integral part of research in most scientific and engineering problems. The critical challenge in optimization lies in iteratively finding the best combination of variables which minimize or maximize one or more objective functions by satisfying the variable requirements and restrictions which are largely known as constraints. Most optimization problems involve one or many constraints due to the limitation in the availability of resources, physical viability, or other functional requirements. The existence of constraints in problems in science and engineering is continuously motivating researchers to develop newer and more efficient methods of constraint handling in optimization. Evolutionary optimiza
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