Experimental Methods for the Analysis of Optimization Algorithms
In operations research and computer science it is common practice to evaluate the performance of optimization algorithms on the basis of computational results, and the experimental approach should follow accepted principles that guarantee the reliability
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Thomas Bartz-Beielstein · Marco Chiarandini · Luís Paquete · Mike Preuss Editors
Experimental Methods for the Analysis of Optimization Algorithms
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Editors Prof. Dr. Thomas Bartz-Beielstein Cologne University of Applied Sciences Institute of Computer Science Faculty of Computer and Engineering Science Campus Gummersbach Steinmüllerallee 1 51643 Gummersbach Germany [email protected] Dr. rer. nat. Luís Paquete University of Coimbra CISUC Department of Informatics Engineering Pólo II 3030-290 Coimbra Portugal [email protected]
Dr. Marco Chiarandini University of Southern Denmark Department of Mathematics and Computer Science Campusvej 55 5230 Odense Denmark [email protected]
Mike Preuss TU Dortmund Department of Computer Science Algorithm Engineering Otto-Hahn-Str. 14 44227 Dortmund Germany [email protected]
ISBN 978-3-642-02537-2 e-ISBN 978-3-642-02538-9 DOI 10.1007/978-3-642-02538-9 Springer Heidelberg Dordrecht London New York ACM Codes: G.1, G.3, F.2, I.2 c Springer-Verlag Berlin Heidelberg 2010 This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer. Violations are liable to prosecution under the German Copyright Law. The use of general descriptive names, registered names, trademarks, 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. Cover design: KuenkelLopka GmbH Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
Foreword
This book belongs on the shelf of anyone interested in carrying out experimental research on algorithms and heuristics for optimization problems. The editors have brought together expertise from diverse sources to address methodological issues arising in this field. The presentation is wide-ranging, containing “big picture” discussions as well as more focused treatment of specific statistical techniques and their application. The emphasis throughout is on careful process and scientific rigor; the discussion is illuminated with many case studies, small tutorials, and references to the literature on optimization. Don’t keep this book on the shelf: read it, and apply the techniques and tools contained herein to your own algorithmic research project. Your experiments will become more efficient and more trustworthy, and your experimental data will lead to clearer and deeper insights about performance. Amherst, Massachusetts, February 2010
Catherine C. McGeoch
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Foreword
Once upon a time, more exactly nearly half
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