Evolutionary Statistical Procedures An Evolutionary Computation Appr
This proposed text appears to be a good introduction to evolutionary computation for use in applied statistics research. The authors draw from a vast base of knowledge about the current literature in both the design of evolutionary algorithms and statisti
- PDF / 4,092,664 Bytes
- 283 Pages / 439.37 x 666.142 pts Page_size
- 8 Downloads / 249 Views
For other titles published in this series, go to http://www.springer.com/series/3022
Roberto Baragona · Francesco Battaglia · Irene Poli
Evolutionary Statistical Procedures An Evolutionary Computation Approach to Statistical Procedures Designs and Applications
123
Prof. Roberto Baragona Sapienza University of Rome Department of Communication and Social Research Via Salaria 113 00198 Rome Italy [email protected]
Prof. Francesco Battaglia Sapienza University of Rome Department of Statistical Sciences Piazzale Aldo Moro 5 00100 Roma Italy [email protected]
Prof. Irene Poli Ca’ Foscari University of Venice Department of Statistics Cannaregio 873 30121 Venice Italy [email protected]
Series Editors: J. Chambers Department of Statistics Sequoia Hall 390 Serra Mall Stanford University Stanford, CA 94305-4065
D. Hand Department of Mathematics Imperial College London, South Kensington Campus London SW7 2AZ United Kingdom
W. Härdle C.A.S.E. Centre for Applied Statistics and Economics School of Business and Economics Humboldt-Universität zu Berlin Unter den Linden 6 10099 Berlin Germany
ISSN 1431-8784 ISBN 978-3-642-16217-6 e-ISBN 978-3-642-16218-3 DOI 10.1007/978-3-642-16218-3 Springer Heidelberg Dordrecht London New York © Springer-Verlag Berlin Heidelberg 2011 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: SPI Publisher Services Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
Preface
In many application fields, artificial intelligence, data mining, pattern recognition operations research, to name but a few, often problems arise that may be reduced at their very essence to optimization problems. Unfortunately, neither the objective function nor the solution search space display that nice properties that may be conveniently exploited by widespread familiar numerical analysis tools. Though these latter offer powerful devices to cope with a great deal of both theoretical and practical problems in so many disciplines, the hypotheses on which they rely are far from being fulfilled within the frameworks that so often constitute the background of the application fields we mentioned so far. Here well behaved analytic functions and compact dom
Data Loading...
