Evolutionary Computation : A Unified Approach

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Evolutionary Computation A Unified Approach Kenneth A. De Jong ABradfordBook The MIT Press Cambridge, Massachusetts London, England
c○ 2006 Massachusetts Institute of Technology All rights reserved. No part of this book may be reproduced in any form by any electronic or mechanical means (including photocopying, recording, or information storage and retrieval) without permission in writing from the publisher. MIT Press books may be purchased at special quantity discounts for business or sales promotional use. For information, please email special sales@mitpress.mit.edu or write to Special Sales Department, The MIT Press, 55 Hayward Street, Cambridge, MA 02142. This book printed and bound in the United States of America. Library of Congress Cataloging-in-Publication Data De Jong, Kenneth A. Evolutionary computation : a unified approach / Kenneth A. De Jong. p. cm. “A Bradford book” Includes bibliographical references and indexes. ISBN 0-262-04194-4 (hc. : alk. paper) 1. Evolutionary programming (Computer science). 2. Evolutionary computation. I. Title. II. Series. QA76.618 .D45 2006 10987654321 00-048674
Page 2: Evolutionary Computation
Page 7 and 8: vi CONTENTS 4 A Unified View of Sim
Page 9 and 10: viii CONTENTS 6.5.7 Selection, Repr
Page 12 and 13: Chapter 1 Introduction The field of
Page 14 and 15: 1.2. EV: A SIMPLE EVOLUTIONARY SYST
Page 16 and 17: 1.2. EV: A SIMPLE EVOLUTIONARY SYST
Page 18 and 19: 1.3. EV ON A SIMPLE FITNESS LANDSCA
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Page 35 and 36: 24 CHAPTER 2. A HISTORICAL PERSPECT
Page 44 and 45: Chapter 3 Canonical Evolutionary Al
Page 46 and 47: 3.3. EVOLUTIONARY PROGRAMMING 35 55
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3.5. GENETIC ALGORITHMS 43 3.5.2 Un
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3.5. GENETIC ALGORITHMS 45 Populati
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3.6. SUMMARY 47 3.6 Summary The can
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Chapter 5 Evolutionary Algorithms a
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5.1. SIMPLE EAS AS PARALLEL ADAPTIV
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5.2. EA-BASED OPTIMIZATION 81 them
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5.2. EA-BASED OPTIMIZATION 83 of th
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5.2. EA-BASED OPTIMIZATION 85 one o
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5.2. EA-BASED OPTIMIZATION 89 Notic
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5.2. EA-BASED OPTIMIZATION 91 120 1
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5.2. EA-BASED OPTIMIZATION 93 parti
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5.2. EA-BASED OPTIMIZATION 95 5 0 O
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5.2. EA-BASED OPTIMIZATION 97 5.2.2
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5.2. EA-BASED OPTIMIZATION 99 of tr
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5.2. EA-BASED OPTIMIZATION 101 700
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5.2. EA-BASED OPTIMIZATION 103 5.2.
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5.3. EA-BASED SEARCH 105 • In the
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5.4. EA-BASED MACHINE LEARNING 107
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5.5. EA-BASED AUTOMATED PROGRAMMING
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5.5. EA-BASED AUTOMATED PROGRAMMING
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5.7. SUMMARY 113 the effect of impr
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116 CHAPTER 6. EVOLUTIONARY COMPUTA
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Chapter 7 Advanced EC Topics The pr
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7.2. DYNAMIC LANDSCAPES 213 nested
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7.2. DYNAMIC LANDSCAPES 215 10 8 Fi
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7.2. DYNAMIC LANDSCAPES 217 10 8 Fi
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7.3. EXPLOITING PARALLELISM 219 7.2
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7.4. EVOLVING EXECUTABLE OBJECTS 22
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7.5. MULTI-OBJECTIVE EAS 223 be of
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7.7. BIOLOGICALLY INSPIRED EXTENSIO
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Chapter 8 The Road Ahead As a well-
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Appendix A Source Code Overview Rat
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A.1. EC1: A VERY SIMPLE EC SYSTEM 2
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A.3. EC3: A MORE FLEXIBLE EC SYSTEM
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A.3. EC3: A MORE FLEXIBLE EC SYSTEM
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Bibliography Altenberg, L. (1994).
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BIBLIOGRAPHY 243 Collins, R. and D.
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BIBLIOGRAPHY 245 Frank, S. A. (1995
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BIBLIOGRAPHY 247 Jansen, T., K. De
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BIBLIOGRAPHY 249 Potter, M. A., J.
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BIBLIOGRAPHY 251 Spears, W. (2000).
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Index 1-point crossover operator, 2
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INDEX 255 graph structures, 74-76,
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Evolutionary Computation : A Unified Approach

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