Spatially Structured Evolutionary Algorithms Artificial Evolution in
Evolutionary algorithms (EAs) is now a mature problem-solving family of heuristics that has found its way into many important real-life problems and into leading-edge scientific research. Spatially structured EAs have different properties than standard, m
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Leiden Center for Natural Computing
Advisory Board: S. Amari G. Brassard K.A. De Jong C.C.A.M. Gielen T. Head L. Kari L. Landweber T. Martinetz ° Z. Michalewicz M.C. Mozer E. Oja G. Paun J. Reif H. Rubin A. Salomaa M. Schoenauer H.-P. Schwefel C. Torras D. Whitley E. Winfree J.M. Zurada
Marco Tomassini
Spatially Structured Evolutionary Algorithms Artificial Evolution in Space and Time With 91 Figures and 21 Tables
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Marco Tomassini Information Systems Department (INFORGE) University of Lausanne 1015 Lausanne Switzerland [email protected] Series Editors G. Rozenberg (Managing Editor) [email protected] Th. Bäck, J.N. Kok, H.P. Spaink Leiden Center for Natural Computing Leiden University Niels Bohrweg 1 2333 CA Leiden, The Netherlands A.E. Eiben Vrije Universiteit Amsterdam The Netherlands
Library of Congress Control Number: 2005929365
ACM Computing Classification (1998): F.1-2, G.1.6, G.2.2, I.2.8 ISBN-10 3-540-24193-0 Springer Berlin Heidelberg New York ISBN-13 978-3-540-24193-5 Springer Berlin Heidelberg New York 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 for prosecution under the German Copyright Law. Springer is a part of Springer Science+Business Media springeronline.com © Springer-Verlag Berlin Heidelberg 2005 Printed in Germany 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: KünkelLopka, Werbeagentur, Heidelberg Typesetting: by the Author Production: LE-TEX Jelonek, Schmidt & Vöckler GbR, Leipzig Printed on acid-free paper 45/3142/YL – 5 4 3 2 1 0
To Anne, Fr´ed´eric, and Vincent
Preface
The field of evolutionary computation (EC) can no longer be considered an esoteric one. Today, after about thirty years of research, a rich corpus of theory exists and many successful real-life applications are witnessing the usefulness of EC heuristics in many fields. EC is a family of population-based methodologies inspired by the interplay of natural selection and variation. However, both the theory and the applications have tended to focus mainly on mixing populations, also called panmictic populations. These are populations in which there is no particular structure: any member of the population is equally likely to “meet” any other member. But Darwin realized long ago that populations ma
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