Evolutionary Computation : A Unified Approach

More documents

Recommendations

Info

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
Page 26 and 27: 1.4. EV ON A MORE COMPLEX FITNESS L
Page 28 and 29: 1.4. EV ON A MORE COMPLEX FITNESS L
Page 30 and 31: 1.5. EVOLUTIONARY SYSTEMS AS PROBLE
Page 32: 1.6. EXERCISES 21 1.6 Exercises 1.
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
Page 48 and 49: 3.4. EVOLUTION STRATEGIES 37 55 50
Page 50 and 51: 3.4. EVOLUTION STRATEGIES 39 55 50
Page 52 and 53: 3.5. GENETIC ALGORITHMS 41 Define s
Page 54 and 55:
3.5. GENETIC ALGORITHMS 43 3.5.2 Un
Page 56 and 57:
3.5. GENETIC ALGORITHMS 45 Populati
Page 58:
3.6. SUMMARY 47 3.6 Summary The can
Page 61 and 62:
50 CHAPTER 4. A UNIFIED VIEW OF SIM
Page 63 and 64:
Page 65 and 66:
Page 67 and 68:
Page 69 and 70:
Page 71 and 72:
Page 73 and 74:
Page 75 and 76:
Page 77 and 78:
Page 79 and 80:
Page 82 and 83:
Chapter 5 Evolutionary Algorithms a
Page 84 and 85:
5.1. SIMPLE EAS AS PARALLEL ADAPTIV
Page 86 and 87:
Page 88 and 89:
Page 90 and 91:
Page 92 and 93:
5.2. EA-BASED OPTIMIZATION 81 them
Page 94 and 95:
5.2. EA-BASED OPTIMIZATION 83 of th
Page 96 and 97:
5.2. EA-BASED OPTIMIZATION 85 one o
Page 98 and 99:
5.2. EA-BASED OPTIMIZATION 87 60 40
Page 100 and 101:
5.2. EA-BASED OPTIMIZATION 89 Notic
Page 102 and 103:
5.2. EA-BASED OPTIMIZATION 91 120 1
Page 104 and 105:
5.2. EA-BASED OPTIMIZATION 93 parti
Page 106 and 107:
5.2. EA-BASED OPTIMIZATION 95 5 0 O
Page 108 and 109:
5.2. EA-BASED OPTIMIZATION 97 5.2.2
Page 110 and 111:
5.2. EA-BASED OPTIMIZATION 99 of tr
Page 112 and 113:
5.2. EA-BASED OPTIMIZATION 101 700
Page 114 and 115:
5.2. EA-BASED OPTIMIZATION 103 5.2.
Page 116 and 117:
5.3. EA-BASED SEARCH 105 • In the
Page 118 and 119:
5.4. EA-BASED MACHINE LEARNING 107
Page 120 and 121:
5.5. EA-BASED AUTOMATED PROGRAMMING
Page 122 and 123:
5.5. EA-BASED AUTOMATED PROGRAMMING
Page 124:
5.7. SUMMARY 113 the effect of impr
Page 127 and 128:
116 CHAPTER 6. EVOLUTIONARY COMPUTA
Page 129 and 130:
Page 131 and 132:
Page 133 and 134:
Page 135 and 136:
Page 137 and 138:
Page 139 and 140:
Page 141 and 142:
Page 143 and 144:
Page 145 and 146:
Page 147 and 148:
Page 149 and 150:
Page 151 and 152:
Page 153 and 154:
Page 155 and 156:
Page 157 and 158:
Page 159 and 160:
Page 161 and 162:
Page 163 and 164:
Page 165 and 166:
Page 167 and 168:
Page 169 and 170:
Page 171 and 172:
Page 173 and 174:
Page 175 and 176:
Page 177 and 178:
Page 179 and 180:
Page 181 and 182:
Page 183 and 184:
Page 185 and 186:
Page 187 and 188:
Page 189 and 190:
Page 191 and 192:
Page 193 and 194:
Page 195 and 196:
Page 197 and 198:
Page 199 and 200:
Page 201 and 202:
Page 203 and 204:
Page 205 and 206:
Page 207 and 208:
Page 209 and 210:
Page 211 and 212:
Page 213 and 214:
Page 215 and 216:
Page 217 and 218:
Page 219 and 220:
Page 222 and 223:
Chapter 7 Advanced EC Topics The pr
Page 224 and 225:
7.2. DYNAMIC LANDSCAPES 213 nested
Page 226 and 227:
7.2. DYNAMIC LANDSCAPES 215 10 8 Fi
Page 228 and 229:
7.2. DYNAMIC LANDSCAPES 217 10 8 Fi
Page 230 and 231:
7.3. EXPLOITING PARALLELISM 219 7.2
Page 232 and 233:
7.4. EVOLVING EXECUTABLE OBJECTS 22
Page 234 and 235:
7.5. MULTI-OBJECTIVE EAS 223 be of
Page 236 and 237:
7.7. BIOLOGICALLY INSPIRED EXTENSIO
Page 238 and 239:
Page 240 and 241:
Page 242 and 243:
Chapter 8 The Road Ahead As a well-
Page 244 and 245:
Appendix A Source Code Overview Rat
Page 246 and 247:
A.1. EC1: A VERY SIMPLE EC SYSTEM 2
Page 248 and 249:
A.3. EC3: A MORE FLEXIBLE EC SYSTEM
Page 250 and 251:
A.3. EC3: A MORE FLEXIBLE EC SYSTEM
Page 252 and 253:
Bibliography Altenberg, L. (1994).
Page 254 and 255:
BIBLIOGRAPHY 243 Collins, R. and D.
Page 256 and 257:
BIBLIOGRAPHY 245 Frank, S. A. (1995
Page 258 and 259:
BIBLIOGRAPHY 247 Jansen, T., K. De
Page 260 and 261:
BIBLIOGRAPHY 249 Potter, M. A., J.
Page 262 and 263:
BIBLIOGRAPHY 251 Spears, W. (2000).
Page 264 and 265:
Index 1-point crossover operator, 2
Page 266 and 267:
INDEX 255 graph structures, 74-76,
show all

Evolutionary Computation : A Unified Approach

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?