Evolution and Optimum Seeking

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270 References Fletcher, R. (1972d), Fortran subroutines for minimization by quasi-Newton methods, UKAEA Research Group, report AERE-R-7125, Harwell, Oxon Fletcher, R., M.J.D. Powell (1963), A rapidly convergent descent method for minimization, Comp. J. 6, 163-168 Fletcher, R., C.M. Reeves (1964), Function minimization by conjugate gradients, Comp. J. 7, 149-154 Flood, M.M., A. Leon (1964), A generalized direct search code for optimization, University ofMichigan, Mental Health Research Institute, preprint 129, Ann Arbor MI, June 1964 Flood, M.M., A. Leon (1966), A universal adaptive code for optimization|GROPE, in: Lavi and Vogl (1966), pp. 101-130 Floudas, C.A., P.M. Pardalos (1990), A collection of test problems for constrained global optimization algorithms, vol. 455 of Lecture Notes in Computer Science, Springer, Berlin Fogarty, L.E., R.M. Howe (1968), Trajectory optimization by a direct descent process, Simulation 11, 127-135 Fogarty, L.E., R.M. Howe (1970), Hybrid computer solution of some optimization problems, Proceedings of the VIth International Analogue Computation Meeting, Munich, Aug.-Sept. 1970, pp. 127-135 Fogel, D.B. (1991), System identi cation through simulated evolution, Ginn Press, Needham Heights MA Fogel, D.B. (1992), Evolving arti cial intelligence, Ph.D. thesis, University of California at San Diego Fogel, D.B., J.W. Atmar (Eds.) (1992), Proceedings of the 1st Annual Conference on Evolutionary Programming, San Diego, Feb. 21-22, 1992, Evolutionary Programming Society, La Jolla CA Fogel, D.B., J.W. Atmar (Eds.) (1993), Proceedings of the 2nd Annual Conference on Evolutionary Programming, San Diego, Feb. 25-26, 1993, Evolutionary Programming Society, La Jolla CA Fogel, L.J. (1962), Autonomous automata, Ind. Research 4, 14-19 Fogel, L.J., A.J. Owens, M.J. Walsh (1965), Arti cial intelligence through a simulation of evolution, in: Max eld, Callahan, and Fogel (1965), pp. 131-155 Fogel, L.J., A.J. Owens, M.J. Walsh (1966a), Adaption of evolutionary programming to the prediction of solar ares, General Dynamics-Convair, report NASA-CR-417, San Diego CA
References 271 Fogel, L.J., A.J. Owens, M.J. Walsh (1966b), Arti cial intelligence through simulated evolution, Wiley, NewYork Forrest, S. (Ed.) (1993), Proceedings of the 5th International Conference on Genetic Algorithms, University of Illinois, Urbana-Champaign IL, July 17-21, 1993, Morgan Kaufmann, San Mateo CA Forsythe, G.E. (1968), On the asymptotic directions of the s-dimensional optimum gradient method, Numer. Math. 11, 57-76 Forsythe, G.E. (1969), Remarks on the paper by Dekker, in: Dejon and Henrici (1969), pp. 49-51 Forsythe, G.E., T.S. Motzkin (1951), Acceleration of the optimum gradient method, Bull. Amer. Math. Soc. 57, 304-305 Fox, R.L. (1971), Optimization methods for engineering design, Addison-Wesley, Reading MA Frankhauser, P., H.-P. Schwefel (1992), Making use of the Weidlich-Haag model in the case of reduced data sets, in: Gritzmann et al. (1992), pp. 320-323 Frankovic, B., S. Petras, J. Skakala, B. Vykouk (1970), Automatisierung und selbsttatige Steuerung, Verlag Technik, Berlin Fraser, A.S. (1957), Simulation of genetic systems by automatic digital computers, Australian J. Biol. Sci. 10, 484-499 Friedberg, R.M. (1958), A learning machine I, IBM J. Res. Dev. 2, 2-13 Friedberg, R.M., B. Dunham, J.H. North (1959), A learning machine II, IBM J. Res. Dev. 3, 282-287 Friedmann, M., L.J. Savage (1947), Planning experiments seeking maxima, in: Eisenhart, Hastay, and Wallis (1947), pp. 365-372 Friedrichs, K.O., O.E. Neugebauer, J.J. Stoker (Eds.) (1948), Studies and essays, Courant anniversary volume, Interscience, New York Fu, K.S., L.D. Cockrell (1970), On search techniques for multimodal surfaces, IFAC Kyoto Symposium on Systems Engineering Approach to Computer Control, Kyoto, Japan, Aug. 1970, paper 17.3 Fu, K.S., Z.J. Nikolic (1966), On some reinforcement techniques and their relation to the stochastic approximation, IEEE Trans. AC-11, 756-758 Furst, H., P.H. Muller, V. Nollau (1968), Eine stochastische Methode zur Ermittlung der Maximalstelle einer Funktion von mehreren Veranderlichen mit experimentell ermittelbaren Funktionswerten und ihre Anwendung bei chemischen Prozessen, Chemie- Technik 20, 400-405
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Evolution and Optimum Seeking Hans-
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vi Multiple Data) machines with man
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viii 3.2.1.6 Complex Strategy of Bo
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Chapter 1 Introduction There is sca
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Introduction 3 simple matter to col
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Chapter 2 Problems and Methods of O
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Particular Problems and Methods of
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Other Special Cases 19 with expecta
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Other Special Cases 21 parts of the
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Chapter 3 Hill climbing Strategies
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One Dimensional Strategies 25 then
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One Dimensional Strategies 27 Even
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One Dimensional Strategies 29 The b
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One Dimensional Strategies 31 F(x)
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One Dimensional Strategies 33 For t
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One Dimensional Strategies 35 It is
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One Dimensional Strategies 37 F P F
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Multidimensional Strategies 39 the
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Multidimensional Strategies 41 asch
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Multidimensional Strategies 43 e 2
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Multidimensional Strategies 45 Step
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Multidimensional Strategies 47 Numb
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Multidimensional Strategies 49 wher
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Multidimensional Strategies 53 Numb
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Multidimensional Strategies 55 The
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Multidimensional Strategies 57 Anum
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Multidimensional Strategies 61 Iter
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Multidimensional Strategies 63 (If
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Multidimensional Strategies 65 eval
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Multidimensional Strategies 67 The
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Multidimensional Strategies 69 devi
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Multidimensional Strategies 71 spec
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Multidimensional Strategies 73 othe
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Multidimensional Strategies 75 anot
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Multidimensional Strategies 77 3.2.
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Multidimensional Strategies 81 Brow
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Multidimensional Strategies 83 (197
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Multidimensional Strategies 85 meth
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Chapter 4 Random Strategies One gro
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Random Strategies 89 parts is taken
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Random Strategies 91 Pardalos and R
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Random Strategies 93 to write the n
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Random Strategies 95 the expectatio
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Random Strategies 97 maintained at
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Random Strategies 99 works entirely
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Random Strategies 101 the principle
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Random Strategies 103 out, a limite
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Chapter 5 Evolution Strategies for
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The Two Membered Evolution Strategy
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A Multimembered Evolution Strategy
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Genetic Algorithms 151 too is the c
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Genetic Algorithms 153 mean objecti
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Genetic Algorithms 155 p( ∆x) 1 1
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Genetic Algorithms 157 out any chan
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Genetic Algorithms 159 Average Numb
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Simulated Annealing 161 There are t
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Tabu Search and Other Hybrid Concep
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Chapter 6 Comparison of Direct Sear
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Theoretical Results 167 Oettli, 196
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Theoretical Results 169 where 0
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Theoretical Results 171 tions. Simi
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Numerical Comparison of Strategies
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Page 245 and 246: Summary and Outlook 237 numerical o
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Page 257 and 258: Chapter 8 References Glossary of ab
Page 259 and 260: References 251 Anscombe, F.J. (1959
Page 261 and 262: References 253 Bandler, J.W. (1969b
Page 263 and 264: References 255 Bendin, F. (1992), E
Page 265 and 266: References 257 Booth, A.D. (1955),
Page 267 and 268: References 259 Brent, R.P. (1971),
Page 269 and 270: References 261 Carroll, C.W. (1961)
Page 271 and 272: References 263 Courant, R., D. Hilb
Page 273 and 274: References 265 Davies, M., I.J. Whi
Page 275 and 276: References 267 Dvoretzky, A. (1956)
Page 277: References 269 Fiacco, A.V. (1974),
Page 281 and 282: References 273 Gill, P.E., W. Murra
Page 283 and 284: References 275 Graves, R.L., P. Wol
Page 285 and 286: References 277 Heckler, R., H.-P. S
Page 287 and 288: References 279 Ho meister, F., H.-P
Page 289 and 290: References 281 Idelsohn, J.M. (1964
Page 291 and 292: References 283 Karnopp, D.C. (1966)
Page 293 and 294: References 285 Kochen, M., H.M. Has
Page 295 and 296: References 287 Kunzi, H.P., H.G. Tz
Page 297 and 298: References 289 LeCam, L.M., J. Neym
Page 299 and 300: References 291 Mamen, R., D.Q. Mayn
Page 301 and 302: 294 References Miele, A., H.Y. Huan
Page 303 and 304: 296 References Murray, W. (Ed.) (19
Page 305 and 306: 298 References Oldenburger, R. (Ed.
Page 307 and 308: 300 References Peschel, M. (1980),
Page 309 and 310: 302 References Powell, M.J.D. (1970
Page 311 and 312: 304 References Rechenberg, I. (1989
Page 313 and 314: 306 References Rybashov, M.V. (1969
Page 315 and 316: 308 References Schmidt, J.W., K. Ve
Page 317 and 318: 310 References Shedler, G.S. (1967)
Page 319 and 320: 312 References Steinbuch, K. (1971)
Page 321 and 322: 314 References Tabak, D. (1970), Ap
Page 323 and 324: 316 References Varela, F.J., P. Bou
Page 325 and 326: 318 References White, L.J., R.G. Da
Page 327 and 328: 320 References Youden, W.J., O. Kem
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322 References Glossary of Abbrevia
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324 References
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326 Appendix A Problem 1.2 Objectiv
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328 Appendix A Minimum: x =(3 0:5)
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330 Appendix A Figure A.3: Graphica
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332 Appendix A Several of the searc
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338 Appendix A Minimum: Start: Prob
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340 Appendix A Problem 2.20 Objecti
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342 Appendix A Problem 2.23 Objecti
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344 Appendix A Start: x (0) i =10 f
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346 Appendix A on the interval 0 y
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348 Appendix A Problem 2.32 after B
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350 Appendix A Constraints: Gj(x) =
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352 Appendix A The constraints form
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354 Appendix A Start: x (0) =(250 2
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356 Appendix A Problem 2.44 As Prob
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358 Appendix A Figure A.29: Graphic
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360 Appendix A Start: Figure A.31:
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362 Appendix A Problem 3.2 (analogo
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364 Appendix A Problem 3.6 (analogo
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366 Appendix A Minimum: x i =0 for
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368 Appendix B LF (integer) Return
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370 Appendix B 4. Convergence crite
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372 Appendix B 8. Function Z(S,R) T
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374 Appendix B 25 L(K)=L(K+1) L(10)
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376 Appendix B LF=2 (continued) No
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378 Appendix B man), vol. 15 of Pro
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380 Appendix B instead of T, as a p
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382 Appendix B 15 CONTINUE 16 FF=F(
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384 Appendix B SK(KS)=AMAX1(SM(I),A
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386 Appendix B B.3 ( + ) Evolution
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388 Appendix B EPSILO (one dimensio
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390 Appendix B GLEICH (real functio
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392 Appendix B GLEICH, and TKONTR d
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394 Appendix B 1 NL=1+N-NS NM=N-1 N
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396 Appendix B ZSTERN=ZBEST K=LBEST
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398 Appendix B C NEGATIVE (LETHAL M
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400 Appendix B C C PREPARE FINAL DA
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402 Appendix B 2 IF(.NOT.BKOMMA.OR.
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404 Appendix B 32 WRITE(KANAL,126)
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406 Appendix B DO 4 I=1,NX KI=KI1 I
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408 Appendix B Subroutine MINMAX MI
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410 Appendix B 2 IF(U.LT..965487131
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412 Appendix B parameters by multip
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414 Appendix B
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416 Appendix C - SIMP Simplex strat
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418 Appendix C C.3.2 How to Install
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420 Appendix C { } return(0.26*(x[0
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422 Appendix C 1 No recombination 2
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424 Appendix C 8e-07 8e-07 Initial
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426 Index Banach, S., 10 Bandler, J
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428 Index Coordinate strategy, 41{4
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430 Index Evolution strategy, 3, 6,
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432 Index Graves, R.L., 23 Great de
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434 Index Khurgin, Ya.I., 89 Kiefer
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436 Index Michie, D., 102 Mickey, M
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438 Index Parkinson, J.M., 61 Parta
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440 Index Rotating coordinates meth
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442 Index Storey, C., 23, 50, 54 St
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444 Index Wilson, S.W., 103 Witt, U
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