Applying Computational Intelligence How to Create Value
In theory, there is no difference between theory and practice. But, in practice, there is. Jan L. A. van de Snepscheut The ?ow of academic ideas in the area of computational intelligence has penetrated industry with tremendous speed and persistence. Thous
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Arthur K. Kordon
Applying Computational Intelligence How to Create Value
Dr. Arthur K. Kordon Data Mining and Modeling Capability Corporate R&D, The Dow Chemical Company 2301 N. Brazosport Blvd. B-1226, Freeport TX 77541 USA [email protected]
ACM Computing Classification (1998): I.2, I.6, G.3, G.1, F.1, I.5, J.1, J.2, H.1. ISBN: 978-3-540-69910-1 e-ISBN: 978-3-540-69913-2 DOI 10.1007/978-3-540-69913-2 Springer Heidelberg Dordrecht London New York Library of Congress Control Number: 2009940398 # 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, Heidelberg, Germany Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
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Preface
In theory, there is no difference between theory and practice. But, in practice, there is. Jan L.A. van de Snepscheut
The flow of academic ideas in the area of computational intelligence has penetrated industry with tremendous speed and persistence. Thousands of applications have proved the practical potential of fuzzy logic, neural networks, evolutionary computation, swarm intelligence, and intelligent agents even before their theoretical foundation is completely understood. And the popularity is rising. Some software vendors have pronounced the new machine learning gold rush to “Transfer Data into Gold”. New buzzwords like “data mining”, “genetic algorithms”, and “swarm optimization” have enriched the top executives’ vocabulary to make them look more “visionary” for the 21st century. The phrase “fuzzy math” became political jargon after being used by US President George W. Bush in one of the election debates in the campaign in 2000. Even process operators are discussing the performance of neural networks with the same passion as the performance of the Dallas Cowboys. However, for most of the engineers and scientists introducing computational intelligence technologies into practice, looking at the growing number of new approaches, and understanding their theoretical principles and potential for value creation becomes a more and more difficult task. In order to keep track of the new techniques (like genetic programming or support vector machines) one has to Google or use
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