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REFERENCES 781 Conference on Hybrid Intelligent Systems, 2007. doi:10.1109/HIS.2007.11. In pro- ceedings [1170]. Online available at http://www.it-weise.de/documents/files/ WZKG2007DGPFg.pdf [accessed 2009-06-26]. [2184] Thomas Weise, Steffen Bleul, Diana Elena Comes, and Kurt Geihs. Different ap- proaches to semantic web service composition. In Abdelhamid Mellouk, Jun Bi, Guadalupe Ortiz, Dickson K. W. Chiu, and Manuela Popescu, editors, Proceed- ings of The Third International Conference on Internet and Web Applications and Services, ICIW 2008, pages 90–96, June 8–13, 2008, Athens, Greece. IEEE Com- puter Society Press, Los Alamitos, CA, USA. ISBN: 978-0-76953-163-2. Library of Congress Control Number: 2008922600. Product Number: E3163. BMS Part Number: CFP0816C-CDR. Online available at http://www.it-weise.de/documents/files/ WBCG2008ICIW.pdf [accessed 2009-06-26]. [2185] Thomas Weise, Stefan Niemczyk, Hendrik Skubch, Roland Reichle, and Kurt Geihs. A tunable model for multi-objective, epistatic, rugged, and neutral fitness landscapes. In Proceedings of Genetic and Evolutionary Computation Conference, GECCO 2008, pages 795–802, 2008. doi:10.1145/1389095.1389252. In proceedings [1117]. Online available at http://www.it-weise.de/documents/files/WNSRG2008GECCO.pdf [ac- cessed 2009-06-26]. [2186] Thomas Weise, Hendrik Skubch, Michael Zapf, and Kurt Geihs. Global Op- timization Algorithms and their Application to Distributed Systems. Kasseler Informatikschriften (KIS) 2008, 3, University of Kassel, FB16, Distributed Sys- tems Group, Wilhelmsh¨oher Allee 73, 34121 Kassel, Germany, University of Kas- sel, October 14, 2008. Persistent Identifier: urn:nbn:de:hebis:34-2008101424484. Online available at https://kobra.bibliothek.uni-kassel.de/handle/urn:nbn: de:hebis:34-2008101424484 and http://www.it-weise.de/documents/files/ WSTG2008GOAATATDS.pdf [accessed 2008-10-17]. [2187] Thomas Weise, Michael Zapf, and Kurt Geihs. Evolving proactive aggre- gation protocols. In Genetic Programming Proceedings of the 11th Euro- pean Conference on Genetic Programming, EuroGP 2008, pages 254–265, 2008. doi:10.1007/978-3-540-78671-9 22. In proceedings [1579]. Online available at http:// www.it-weise.de/documents/files/WZG2008DGPFa.pdf and http://dx.doi.org/ 10.1007/978-3-540-78671-9_22 [accessed 2009-06-26]. [2188] Thomas Weise, Alexander Podlich, Kai Reinhard, Christian Gorldt, and Kurt Geihs. Evolutionary freight transportation planning. In EvoTRANSLOG, 3rd European Workshop on Evolutionary Computation in Transportation and Logistics, pages 768– 777, 2009. doi:10.1007/978-3-642-01129-0 87. In proceedings [802]. Nominated for best paper award. Online available at http://www.it-weise.de/documents/files/ WPRGG2009EFTP.pdf [accessed 2009-06-26]. [2189] August Weismann. The Germ-Plasm – A Theory of Heredity. Charles Scrib- ner’s Sons, New York, USA, 1893. Translated by W. Newton Parker, Ph.D. and Harriet R¨onnfeld B.Sc. Online available at http://www.esp.org/books/weismann/ germ-plasm/facsimile/ [accessed 2008-09-10]. [2190] Gerhard Weiß and Sandip Sen, editors. Adaption and Learning in Multi-Agent Sys- tems, IJCAI’95 Workshop Proceedings, volume 1042 of Lecture Notes in Computer Science (LNCS), 1996, Montr´ eal, Qu´ ebec, Canada. Springer. ISBN: 3-5406-0923-7. See also [1453, 1454, 1364]. [2191] Eric W. Weisstein. K-means clustering algorithm, 1999–2006. From MathWorld– A Wolfram Web Resource. Online available at http://mathworld.wolfram.com/ K-MeansClusteringAlgorithm.html [accessed 2007-08-11]. [2192] Justin Werfel and Radhika Nagpal. Extended stigmergy in collective construction. IEEE Intelligent Systems, 21(2):20–28, March/April 2006. doi:10.1109/MIS.2006.25. Online available at http://hebb.mit.edu/people/jkwerfel/ieeeis06.pdf [accessed 2008-06-12].

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REFERENCES 781

Conference on Hybrid Intelligent Systems, 2007. doi:10.1109/HIS.2007.11. In pro-ceedings [1170]. Online available at http://www.it-weise.de/documents/files/

WZKG2007DGPFg.pdf [accessed 2009-06-26].[2184] Thomas Weise, Steffen Bleul, Diana Elena Comes, and Kurt Geihs. Different ap-

proaches to semantic web service composition. In Abdelhamid Mellouk, Jun Bi,Guadalupe Ortiz, Dickson K. W. Chiu, and Manuela Popescu, editors, Proceed-ings of The Third International Conference on Internet and Web Applications andServices, ICIW 2008, pages 90–96, June 8–13, 2008, Athens, Greece. IEEE Com-puter Society Press, Los Alamitos, CA, USA. ISBN: 978-0-76953-163-2. Library ofCongress Control Number: 2008922600. Product Number: E3163. BMS Part Number:CFP0816C-CDR. Online available at http://www.it-weise.de/documents/files/WBCG2008ICIW.pdf [accessed 2009-06-26].

[2185] Thomas Weise, Stefan Niemczyk, Hendrik Skubch, Roland Reichle, and Kurt Geihs.A tunable model for multi-objective, epistatic, rugged, and neutral fitness landscapes.In Proceedings of Genetic and Evolutionary Computation Conference, GECCO 2008,pages 795–802, 2008. doi:10.1145/1389095.1389252. In proceedings [1117]. Onlineavailable at http://www.it-weise.de/documents/files/WNSRG2008GECCO.pdf [ac-

cessed 2009-06-26].[2186] Thomas Weise, Hendrik Skubch, Michael Zapf, and Kurt Geihs. Global Op-

timization Algorithms and their Application to Distributed Systems. KasselerInformatikschriften (KIS) 2008, 3, University of Kassel, FB16, Distributed Sys-tems Group, Wilhelmshoher Allee 73, 34121 Kassel, Germany, University of Kas-sel, October 14, 2008. Persistent Identifier: urn:nbn:de:hebis:34-2008101424484.Online available at https://kobra.bibliothek.uni-kassel.de/handle/urn:nbn:de:hebis:34-2008101424484 and http://www.it-weise.de/documents/files/

WSTG2008GOAATATDS.pdf [accessed 2008-10-17].[2187] Thomas Weise, Michael Zapf, and Kurt Geihs. Evolving proactive aggre-

gation protocols. In Genetic Programming – Proceedings of the 11th Euro-pean Conference on Genetic Programming, EuroGP 2008, pages 254–265, 2008.doi:10.1007/978-3-540-78671-9 22. In proceedings [1579]. Online available at http://www.it-weise.de/documents/files/WZG2008DGPFa.pdf and http://dx.doi.org/

10.1007/978-3-540-78671-9_22 [accessed 2009-06-26].[2188] Thomas Weise, Alexander Podlich, Kai Reinhard, Christian Gorldt, and Kurt Geihs.

Evolutionary freight transportation planning. In EvoTRANSLOG, 3rd EuropeanWorkshop on Evolutionary Computation in Transportation and Logistics, pages 768–777, 2009. doi:10.1007/978-3-642-01129-0 87. In proceedings [802]. Nominated forbest paper award. Online available at http://www.it-weise.de/documents/files/WPRGG2009EFTP.pdf [accessed 2009-06-26].

[2189] August Weismann. The Germ-Plasm – A Theory of Heredity. Charles Scrib-ner’s Sons, New York, USA, 1893. Translated by W. Newton Parker, Ph.D. andHarriet Ronnfeld B.Sc. Online available at http://www.esp.org/books/weismann/germ-plasm/facsimile/ [accessed 2008-09-10].

[2190] Gerhard Weiß and Sandip Sen, editors. Adaption and Learning in Multi-Agent Sys-tems, IJCAI’95 Workshop Proceedings, volume 1042 of Lecture Notes in ComputerScience (LNCS), 1996, Montreal, Quebec, Canada. Springer. ISBN: 3-5406-0923-7.See also [1453, 1454, 1364].

[2191] Eric W. Weisstein. K-means clustering algorithm, 1999–2006. From MathWorld–A Wolfram Web Resource. Online available at http://mathworld.wolfram.com/

K-MeansClusteringAlgorithm.html [accessed 2007-08-11].[2192] Justin Werfel and Radhika Nagpal. Extended stigmergy in collective construction.

IEEE Intelligent Systems, 21(2):20–28, March/April 2006. doi:10.1109/MIS.2006.25.Online available at http://hebb.mit.edu/people/jkwerfel/ieeeis06.pdf [accessed

2008-06-12].

782 REFERENCES

[2193] Justin Werfel, Yaneer Bar-Yam, Daniela Rus, and Radhika Nagpal. Distributedconstruction by mobile robots with enhanced building blocks. In IEEE InternationalConference on Robotics and Automation (ICRA), May 15–19, 2006, Hilton in theWalt Disney World Resort hotel, Walt Disney World Resort, Orlando, Florida (LakeBuena Vista), USA. Online available at http://www.eecs.harvard.edu/~rad/ssr/papers/icra06-werfel.pdf [accessed 2008-06-12].

[2194] Gregory M. Werner and Michael G. Dyer. Evolution of communication in artificialorganisms. In Artificial Life II, pages 659–687, 1992. Redwood City, CA. In proceed-ings [1248]. Online available at http://www.isrl.uiuc.edu/~amag/langev/paper/werner92evolutionOf.html [accessed 2008-07-28].

[2195] Thomas H. Westerdale. The bucket brigade is not genetic. In Proceedings of the 1stInternational Conference on Genetic Algorithms, pages 45–59, 1985. In proceedings[856].

[2196] Thomas H. Westerdale. A reward scheme for production systems with overlappingconflict sets. IEEE Transactions on Systems, Man and Cybernetics, 16(3):369–383,1986. ISSN: 0018-9472.

[2197] Thomas H. Westerdale. Altruism in the bucket brigade. In Proceedings of the SecondInternational Conference on Genetic algorithms and their application, pages 22–26,1987. In proceedings [857].

[2198] A. Wetzel. Evaluation of the Effectiveness of Genetic Algorithms in CombinatorialOptimization. PhD thesis, University of Pittsburgh, Pittsburgh, PA, 1983. Unpub-lished manuscript, technical report.

[2199] Ingrid Wetzel. Information systems development with anticipation of change:Focussing on professional bureaucracies. In Proceedings of Hawaii InternationalConference on Systems Sciences, HICSS 34. IEEE Computer Society, January2001, Maui, Hawaii, USA. Online available at http://citeseer.ist.psu.edu/

532081.html and http://swt-www.informatik.uni-hamburg.de/publications/

download.php?id=177 [accessed 2007-09-02].[2200] James F. Whidborne, D.-W. Gu, and Ian Postlethwaite. Algorithms for the method

of inequalities – a comparative study. In Procedings of the 1995 American ControlConference, volume 5, pages 3393–3397, June 21–23, 1995, Seattle, Washington, USA.ISBN: 0-7803-2445-5. INSPEC Accession Number: 5080557. FA19 = 9:15.

[2201] Peter Alexander Whigham. Context-free grammar and genetic programming. Techni-cal Report Technical Report CS20/94, Department of Computer Science, AustralianDefence Force Academy, University of New South Wales, Canberra ACT 2600, Aus-tralia, 1994.

[2202] Peter Alexander Whigham. Grammatically-based genetic programming. In Proceed-ings of the Workshop on Genetic Programming: From Theory to Real-World Ap-plications, pages 33–41, 1995. In proceedings [1757]. Online available at http://

citeseer.ist.psu.edu/whigham95grammaticallybased.html [accessed 2007-08-15].[2203] Peter Alexander Whigham. Inductive bias and genetic programming. In First Inter-

national Conference on Genetic Algorithms in Engineering Systems: Innovations andApplications, GALESIA, pages 461–466, 1995. In proceedings [2309]. Online availableat http://citeseer.ist.psu.edu/343730.html [accessed 2008-08-15].

[2204] Peter Alexander Whigham. Grammatical bias for evolutionary learning. PhD thesis,School of Computer Science, University College, University of New South Wales, Aus-tralian Defence Force Academy, Canberra, New South Wales, Australia, October 14,1996. Order Number: AAI0597571.

[2205] Peter Alexander Whigham. Search bias, language bias, and genetic program-ming. In Genetic Programming 1996: Proceedings of the First Annual Conference,pages 230–237, 1996. In proceedings [1207]. Online available at http://citeseer.

ist.psu.edu/whigham96search.html and ftp://www.cs.adfa.edu.au/pub/xin/

whigham_gp96.ps.gz [accessed 2007-09-09].

REFERENCES 783

[2206] R. C. White, jr. A survey of random methods for parameter optimization. Simulation,17(5):197–205, 1971. doi:10.1177/003754977101700504. Online available at http://sim.sagepub.com/cgi/reprint/17/5/197?ck=nck [accessed 2008-03-26].

[2207] L. Darell Whitley. A genetic algorithm tutorial. Technical Report CS-93-103, Com-puter Science Department, Colorado State University, Fort Collins, March 10, 1993.Online available at http://citeseer.ist.psu.edu/177719.html [accessed 2007-11-29].See also [2208].

[2208] L. Darell Whitley. A genetic algorithm tutorial. Statistics and Comput-ing, 4(2):65–85, June 1994. ISSN: ISSN 0960-3174 (Print) 1573-1375 (On-line). doi:10.1007/BF00175354. Online available at http://samizdat.mines.

edu/ga_tutorial/ga_tutorial.ps and http://www.citeulike.org/user/Bc91/

article/1449453 [accessed 2007-08-12]. Also published as technical report [2207].[2209] L. Darrell Whitley, editor. Proceedings of the Second Workshop on Foundations

of Genetic Algorithms (FOGA), July 26–29, 1992, Vail, Colorado, USA. MorganKaufmann, San Mateo, CA, USA. ISBN: 1-5586-0263-1. Published February 1, 1993.

[2210] L. Darrell Whitley, editor. Late Breaking Papers at Genetic and Evolutionary Com-putation Conference (GECCO’00), July 8–12, 2000, The Riviera Hotel and Casino,Las Vegas, Nevada, USA. See also [2216].

[2211] L. Darrell Whitley. The GENITOR algorithm and selective pressure: Why rank-based allocation of reproductive trials is best. In Proceedings of the 3rd InternationalConference on Genetic Algorithms, pages 116–121, 1989. In proceedings [1820]. On-line available at http://citeseer.ist.psu.edu/531140.html and http://www.cs.

colostate.edu/~genitor/1989/ranking89.ps.gz [accessed 2007-08-21].[2212] L. Darrell Whitley. Cellular genetic algorithms. In Proceedings of the 5th Interna-

tional Conference on Genetic Algorithms, page 658, 1993. In proceedings [730].[2213] L. Darrell Whitley and Timothy Starkweather. Genitor ii.: A distributed genetic

algorithm. Journal of Experimental & Theoretical Artificial Intelligence, 2(3):189–214, July 1990. ISSN: 0952-813X. doi:10.1080/09528139008953723.

[2214] L. Darrell Whitley and Michael D. Vose, editors. Proceedings of the Third Workshopon Foundations of Genetic Algorithms (FOGA), July 31–August 2, 1994, Estes Park,Colorado, USA. Morgan Kaufmann, San Francisco, CA, USA. ISBN: 1-5586-0356-5.Published June 1, 1995.

[2215] L. Darrell Whitley, V. Scott Gordon, and Keith E. Mathias. Lamarckian evolu-tion, the baldwin effect and function optimization. In PPSN III: Proceedings of theInternational Conference on Evolutionary Computation. The Third Conference onParallel Problem Solving from Nature, pages 6–15, 1994. In proceedings [492]. On-line available at http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.

18.2428 [accessed 2008-09-11].[2216] L. Darrell Whitley, David Goldberg, Erick Cantu-Paz, Lee Spector, Ian C. Parmee,

and Hans-Georg Beyer, editors. Proceedings of the Genetic and Evolutionary Compu-tation Conference (GECCO’00), July 8–12, 2000, The Riviera Hotel and Casino, LasVegas, Nevada, USA. Morgan Kaufmann. ISBN: 978-1-55860-708-8. See also [2210].

[2217] Dirk Wiesmann, Ulrich Hammel, and Thomas Back. Robust design of multilayeroptical coatings by means of evolutionary algorithms. IEEE Transactions on Evo-lutionary Computation, 2(4):162–167, November 1998. ISSN: 1089-778X. CODEN:ITEVF5. INSPEC Accession Number: 6149966. doi:10.1109/4235.738986. See also[2218].

[2218] Dirk Wiesmann, Ulrich Hammel, and Thomas Back. Robust design of multilayeroptical coatings by means of evolutionary strategies. Sonderforschungsbereich (sfb)531, Universitat Dortmund, March 31 1998. Online available at http://hdl.handle.net/2003/5348 and http://citeseer.ist.psu.edu/325801.html [accessed 2008-07-19].See also [2217].

784 REFERENCES

[2219] Wikipedia. Wikipedia – the free encyclopedia, 2008. Online available at http://en.wikipedia.org/ [accessed 2009-06-26].

[2220] Frank Wilcoxon. Individual comparisons by ranking methods. Biometrics Bulletin, 1(6):80–83, December 1945. ISSN: 00994987. Online available at http://sci2s.ugr.es/keel/pdf/algorithm/articulo/wilcoxon1945.pdf [accessed 2008-08-06].

[2221] Frank Wilcoxon and Roberta A. Wilcox. Some Rapid Approximate Statistical Pro-cedures. American Cyanamid Company, Stamford Research Laboratories, Stamford,Connecticut, USA, 1964. Original: 1949.

[2222] Frank Wilcoxon, S. V. Katti, and Roberta A. Wilcox. Critical values and proba-bility levels for the wilcoxon rank sum test and the wilcoxon signed rank test. InH. Leon Harter and D. B. Owen, editors, Selected Tables in Mathematical Statistics,volume 1, pages 171–259. American Mathematical Society / Markham, Providence,Rode Island, USA, 1975. ISBN: 0-8218-1901-1, 0-8410-2501-0, 978-0-84102-501-1,978-0-82181-901-2.

[2223] Herbert S. Wilf. Algorithms and Complexity. AK Peters, Ltd., second edition, De-cember 2002. ISBN: 978-1-56881-178-9.

[2224] Claus O. Wilke. Evolutionary Dynamics in Time-Dependent Environments. PhDthesis, Fakultat fur Physik und Astronomie, Ruhr-Universitat Bochum, Shaker Ver-lag, Aachen, July 1999. ISBN: 978-3-82656-199-3. Online available at http://wlab.biosci.utexas.edu/~wilke/ps/PhD.ps.gz [accessed 2007-08-19].

[2225] Claus O. Wilke. Adaptive evolution on neutral networks. Bulletin of Mathemati-cal Biology, 63(4):715–730, July 2001. ISSN: 0092-8240 (Print) 1522-9602 (Online).Online available at http://arxiv.org/abs/physics/0101021v1 [accessed 2008-07-02].

[2226] Daniel N. Wilke, Schalk Kok, and Albert A. Groenwold. Comparison of linear andclassical velocity update rules in particle swarm optimization: notes on diversity.International Journal for Numerical Methods in Engineering, 70(8):962–984, 2007.Online available at http://doi.wiley.com/10.1002/nme.1867 [accessed 2007-08-20].

[2227] George C. Williams. Pleiotropy, natural selection, and the evolution of senescence.Evolution, 11(4):398–411, December 1957. doi:10.2307/2406060. See also [2228].

[2228] George C. Williams. Pleiotropy, natural selection, and the evolution of senescence.SAGE KE, Science of Aging Knowledge Environment, 2001(1), October 3, 2001. cp13.See also [2227].

[2229] Daniel B. Willingham and Laura Preuss. The death of implicit memory. Psy-che, 2(15), October 1995. Online available at http://www.journalpsyche.org/

ojs-2.2/index.php/psyche/article/viewFile/2419/2348 and http://psyche.

cs.monash.edu.au/v2/psyche-2-15-willingham.html [accessed 2008-12-01].[2230] Dominic Wilson and Devinder Kaur. Using quotient graphs to model neutrality in

evolutionary search. In Genetic and Evolutionary Computation Conference, pages2233–2238, 2008. In proceedings [1117].

[2231] Edward Osborne Wilson. Sociobiology: The New Synthesis. Belknap Press / HarvardUniversity Press, Cambridge, Massachusets, USA / Cumbreland, Rhode Island, USA,1975. See also [2232].

[2232] Edward Osborne Wilson. Sociobiology: The New Synthesis. Belknap Press,twenty-fifth anniversary edition edition, March 2000. ISBN: 0-6740-0235-0,978-0-67400-235-7. See also [2231].

[2233] P. B. Wilson and M. D. Macleod. Low implementation cost iir digital filter designusing genetic algorithms. In Proceedings of the IEE/IEEE Workshop on Natural Al-gorithms in Signal Processing, pages 4/1–4/8. IEEE, November 14–16, 1993, Chelms-ford, Essex, UK.

[2234] Stewart Wilson. Bid competition and specificity reconsidered. Complex Systems, 2(6):705–723, 1988. ISSN: 0891-2513.

REFERENCES 785

[2235] Stewart W. Wilson. ZCS: A zeroth level classifier system. Evolutionary Computation,2(1):1–18, 1994. Online available at http://citeseer.ist.psu.edu/wilson94zcs.html and http://www.eskimo.com/~wilson/ps/zcs.pdf [accessed 2007-09-12].

[2236] Stewart W. Wilson. Classifier fitness based on accuracy. Evolutionary Compu-tation, 3(2):149–175, 1995. Online available at http://citeseer.ist.psu.edu/

wilson95classifier.html [accessed 2007-09-12].[2237] Stewart W. Wilson. Generalization in the XCS classifier system. In Proceedings

of the Third Annual Conference on Genetic Programming 1998, pages 665–674,1998. In proceedings [1209]. Online available at http://citeseer.ist.psu.edu/

wilson98generalization.html [accessed 2007-09-12].[2238] Stewart W. Wilson. State of XCS classifier system research. In Learning Classifier

Systems, From Foundations to Applications, pages 63–82. Springer-Verlag, 2000. Incollection [1252]. Online available at http://citeseer.ist.psu.edu/72750.html

and http://www.eskimo.com/~wilson/ps/state.ps.gz [accessed 2007-08-23].[2239] Stewart W. Wilson and David E. Goldberg. A critical review of classifier systems.

In Proceedings of the 3rd International Conference on Genetic Algorithms, pages244–255, 1989. In proceedings [1820]. Online available at http://www.eskimo.com/

~wilson/ps/wg.ps.gz [accessed 2007-09-12].[2240] Ojvind Winge. Wilhelm johannsen: The creator of the terms gene, genotype, pheno-

type and pure line. Journal of Heredity, 49(2):83–88, March 1958. ISSN: 1465-7333(Online), 0022-1503 (Print). Online available at http://jhered.oxfordjournals.

org/cgi/reprint/49/2/83.pdf [accessed 2008-08-21]. See also [1056].[2241] Hans Winkler. Verbreitung und Ursache der Parthenogenesis im Pflanzen- und Tier-

reiche. Verlag Gustav Fischer, Jena, 1920.[2242] Paul C. Winter, G. Ivor Hickey, and Hugh L. Fletcher. Instant Notes in Genetics.

Springer, New York / BIOS Scientific Publishers / Taylor & Francis Ltd., 1st: 1998,2nd ed: 2002, 3rd ed: 2006. ISBN: 1-8599-6166-5, 0-3879-1562-1, 978-0-38791-562-3,1-8599-6262-9, 978-1-85996-262-6, 978-0-41537-619-8, 0-4153-7619-X.

[2243] Ian H. Witten and Eibe Frank. Data Mining: Practical Machine Learning Toolsand Techniques with Java Implementations. The Morgan Kaufmann Series inData Management Systems. Morgan Kaufmann, first edition, October 1999. ISBN:978-1-55860-552-7.

[2244] David H. Wolpert and William G. Macready. No free lunch theorems forsearch. Technical Report SFI-TR-95-02-010, The Santa Fe Institute, 1399 HydePark Rd., Santa Fe, NM, 87501, USA, February 6, 1995. Online availableat http://citeseer.ist.psu.edu/wolpert95no.html and http://www.santafe.

edu/research/publications/workingpapers/95-02-010.pdf [accessed 2008-03-28].[2245] David H. Wolpert and William G. Macready. No free lunch theorems for op-

timization. IEEE Transactions on Evolutionary Computation, 1(1):67–82, April1997. doi:10.1109/4235.585893. Online available at http://citeseer.ist.psu.edu/wolpert96no.html [accessed 2008-03-28].

[2246] Koon-Pong Wong, S. R. Meikle, Dagan Feng, and M. J. Fulham. Estimation of inputfunction and kinetic parameters using simulated annealing: application in a flowmodel. IEEE Transactions on Nuclear Science, 49(3):707–713, June 2002. ISSN:0018-9499. doi:10.1109/TNS.2002.1039552.

[2247] Man Leung Wong and Kwong Sak Leung. Learning first-order relations from noisydatabases using genetic algorithms. In Proceedings of the Second Singapore Inter-national Conference on Intelligent Systems, pages 159–164, 1994. Online availableat http://cptra.ln.edu.hk/staffProfile/mlwongPub.htm and http://www.cs.

bham.ac.uk/~wbl/biblio/gp-html/ManLeungWong.html [accessed 2007-08-15].[2248] Man Leung Wong and Kwong Sak Leung. An adaptive inductive logic program-

ming system using genetic programming. In Evolutionary Programming IV Pro-ceedings of the Fourth Annual Conference on Evolutionary Programming, pages

786 REFERENCES

737–752, 1995. In proceedings [1380], Online available at http://cptra.ln.edu.

hk/staffProfile/mlwongPub.htm and http://www.cs.bham.ac.uk/~wbl/biblio/

gp-html/ManLeungWong.html [accessed 2007-08-15].[2249] Man Leung Wong and Kwong Sak Leung. Inducing logic programs with genetic algo-

rithms: the genetic logicprogramming system genetic logic programming and applica-tions. IEEE Expert, 10(5):68–76, October 1995. doi:10.1109/64.464935. IEEE ExpertSpecial Track on Evolutionary Programming (Peter John Angeline ed.). Online avail-able at http://cptra.ln.edu.hk/staffProfile/mlwongPub.htm and http://www.

cs.bham.ac.uk/~wbl/biblio/gp-html/ManLeungWong.html [accessed 2007-08-15].[2250] Man Leung Wong and Kwong Sak Leung. Combining genetic programming and

inductive logic programming using logic grammars. In IEEE Conference on Evolu-tionary Computation, volume 2, pages 733–736, 1995. In proceedings [1000]. Onlineavailable at http://cptra.ln.edu.hk/staffProfile/mlwongPub.htm and http://

www.cs.bham.ac.uk/~wbl/biblio/gp-html/ManLeungWong.html [accessed 2007-08-15].[2251] Man Leung Wong and Kwong Sak Leung. Applying logic grammars to induce sub-

functions in geneticprogramming. In Proceedings of IEEE International Confer-ence on Evolutionary Computation, volume 2, pages 737–740, 1995. In proceedings[1000]. Online available at http://cptra.ln.edu.hk/staffProfile/mlwongPub.

htm and http://www.cs.bham.ac.uk/~wbl/biblio/gp-html/ManLeungWong.html

[accessed 2007-08-15].[2252] Man Leung Wong and Kwong Sak Leung. Evolutionary program induction directed

by logic grammars. Evolutionary Computation, 5(2):143–180, summer 1997. Spe-cial Issue: Trends in Evolutionary Methods for Program Induction. Online availableat http://cptra.ln.edu.hk/staffProfile/mlwongPub.htm and http://www.cs.

bham.ac.uk/~wbl/biblio/gp-html/ManLeungWong.html [accessed 2007-08-15].[2253] Man Leung Wong and Kwong Sak Leung. Data Mining Using Grammar Based Ge-

netic Programming and Applications, volume 3 of Genetic Programming. Springer,January 2000. ISBN: 978-0-79237-746-7.

[2254] John R. Woodward. Evolving turing complete representations. In Proceedings ofthe 2003 Congress on Evolutionary Computation, volume 2, pages 830–837, 2003.doi:10.1109/CEC.2003.1299753. In proceedings [1803]. Online available at http://

citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.3.6859 and http://www.

cs.bham.ac.uk/~wbl/biblio/gp-html/Woodward_2003_Etcr.html [accessed 2008-11-08].[2255] Nimit Worakul, Wibul Wongpoowarak, and Prapaporn Boonme. Optimization in

development of acetaminophen syrup formulation. Drug Development and IndustrialPharmacy, 28(3):345–351, 2002. ISSN: 1520-5762 (electronic) 0363-9045 (paper). seeerratum [2256].

[2256] Nimit Worakul, Wibul Wongpoowarak, and Prapaporn Boonme. Optimization indevelopment of acetaminophen syrup formulation – erratum. Drug Development andIndustrial Pharmacy, 28(8):1043–10045, 2002. ISSN: 1520-5762 (electronic) 0363-9045(paper). see paper [2255].

[2257] Robert P. Worden. A speed limit for evolution. Journal of Theoretical Biology, 176(1):137–152, September 7, 1995. ISSN: 0022-5193. doi:10.1006/jtbi.1995.0183. Onlineavailable at http://dx.doi.org/10.1006/jtbi.1995.0183 [accessed 2008-08-10].

[2258] Alden H. Wright, Richard K. Thompson, and Jian Zhang. The computationalcomplexity of n-k fitness functions. IEEE Transactions on Evolutionary Compu-tation, 4(4):373–379, November 2000. ISSN: 1089-778X. INSPEC Accession Num-ber: 6791340. doi:10.1109/4235.887236. Online available at http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.40.3093 and http://www.cs.umt.edu/u/

wright/papers/nkcomplexity.ps.gz [accessed 2009-02-26].[2259] Alden H. Wright, Michael D. Vose, Kenneth Alan De Jong, and Lothar M. Schmitt,

editors. Revised Selected Papers of the 8th International Workshop on Foundationsof Genetic Algorithms, FOGA 2005, volume 3469/2005 of Lecture Notes in Computer

REFERENCES 787

Science (LNCS), January 5–9, 2005, Aizu-Wakamatsu City, Japan. Springer Verlag,Berlin Heidelberg. ISBN: 978-3-54027-237-3. doi:10.1007/11513575 1. PublishedAugust 22, 2005. see http://www.cs.umt.edu/foga05/ [accessed 2007-09-01].

[2260] Margaret H. Wright. Direct search methods: Once scorned, now respectable. InD. F. Griffiths and G. A. Watson, editors, Numerical Analysis – Proceedings of the16th Dundee Biennial Conference in Numerical Analysis, Pitman Research Notes inMathematics, pages 191–208, June 27–30, 1995, University of Dundee, Scotland, UK.Addison Wesley Longman Limited / Chapman & Hall/CRC, Harlow, UK. ISBN:0-5822-7633-0, 978-0-58227-633-8. Online available at http://citeseer.ist.psu.

edu/155516.html [accessed 2008-06-14].[2261] Sewall Wright. The roles of mutation, inbreeding, crossbreeding and selection in evolu-

tion. In D. F. Jones, editor, Proceedings of the Sixth Annual Congress of Genetics, vol-ume 1, pages 356–366, 1932. Online available at http://www.blackwellpublishing.com/ridley/classictexts/wright.pdf [accessed 2007-08-11].

[2262] Chun-Hsin Wu. Peer-to-peer systems: Macro-computing with micro-computers,July 2003. Presented at 3rd International Conference on Open Source inTaipei, Taiwan. Presentation available at http://www.csie.nuk.edu.tw/~wuch/

publications/2003-icos-p2p-wuch.pdf [accessed 2007-08-13].[2263] Ge Wu, Volkert Hansen, E. Kreysa, and H.-P. Gemund. Optimierung von fss-

bandpassfiltern mit hilfe der schwarmintelligenz (particle swarm optimization). Ad-vances in Radio Science, 4:65–71, September 2006. Online available at http://www.adv-radio-sci.net/4/65/2006/ars-4-65-2006.pdf [accessed 2007-08-21].

[2264] Hsien-Chung Wu. Evolutionary Computation. Department of Mathematics, Na-tional Kaohsiung Normal University, Kaohsiung 802, Taiwan, February 2005. Lec-ture notes. Online available at http://nknucc.nknu.edu.tw/~hcwu/pdf/evolec.

pdf [accessed 2007-07-16].[2265] Mingfang Wu, Michael Fuller, and Ross Wilkinson. Using clustering and classifica-

tion approaches in interactive retrieval. Inf. Process. Manage., 37(3):459–484, 2001.Online available at http://citeseer.ist.psu.edu/wu01using.html and http://

de.scientificcommons.org/313591 [accessed 2007-08-11].[2266] Nelson Wu. Differential evolution for optimisation in dynamic environments. Techni-

cal Report, School of Computer Science and Information Technology, RMIT Univer-sity, November 2006. Online available at http://yallara.cs.rmit.edu.au/~newu/portfolio.html [accessed 2007-08-19].

X

[2267] Fatos Xhafa, Francisco Herrera, Ajith Abraham abd Mario Koppen, and Jose ManuelBenitez, editors. 8th International Conference on Hybrid Intelligent Systems (HIS2008), September 10–12, 2008, Edifici Vertex, Placa Eusebi Guell 6, TechnicalUniversity of Catalonia, Barcelona, Spain. IEEE Computer Society, IEEE Service,Center, 445 Hoes Lane, P.O. Box 133, Piscataway, NJ 08855-1331, USA. ISBN:978-0-76953-326-1. Library of Congress Control Number: 2008928438. IEEE Com-puter Society Order Number P3326. BMS Part Number CFP08360-CDR. see http://his2008.lsi.upc.edu/ [accessed 2009-03-02].

[2268] Bowei Xi, Zhen Liu, Mukund Raghavachari, Cathy H. Xia, and Li Zhang. A smarthill-climbing algorithm for application server configuration. In WWW’04: Proceedingsof the 13th international conference on World Wide Web, pages 287–296, May 17–20,2004, New York, NY, USA. ACM Press, New York, NY, USA. ISBN: 1-5811-3844-X.doi:10.1145/988672.988711. Session: Server performance and scalability. Online avail-able at http://citeseer.ist.psu.edu/xi04smart.html [accessed 2007-09-11].

788 REFERENCES

[2269] Yong L. Xiao and Donald E. Williams. Game: Genetic algorithm for minimizationof energy, an interactive program for three-dimensional intermolecular interactions.Computers & Chemistry, 18(2):199–201, 1994.

[2270] Shengwu Xiong, Weiwu Wang, and Feng Li. A new genetic programming approach insymbolic regression. In Proceedings of 15th IEEE International Conference on Toolswith Artificial Intelligence (ICTAI’03), pages 161–167, November 3–5, 2003. IEEEComputer Society, Los Alamitos, CA, USA. doi:10.1109/TAI.2003.1250185.

[2271] Kai Xu, Sushil J. Louis, and Roberto C. Mancini. A scalable parallel geneticalgorithm for x-ray spectroscopic analysis. In GECCO’05: Proceedings of the2005 conference on Genetic and evolutionary computation, pages 811–816, 2005.doi:http://doi.acm.org/10.1145/1068009.1068145. In proceedings [202]. Online avail-able at http://doi.acm.org/10.1145/1068009.1068145 [accessed 2007-08-14].

Y

[2272] Hirozumi Yamaguchi, Kozo Okano, Teruo Higashino, and Kenichi Taniguchi. Syn-thesis of protocol entities’ specifications from service specifications in a petri netmodel with registers. In ICDCS’95: Proceedings of the 15th International Con-ference on Distributed Computing Systems, pages 510–517, May 30–June 2, 1995,Vancouver, British Columbia, Canada. IEEE Computer Society, Washington, DC,USA. ISBN: 0-8186-7025-8. Online available at http://citeseer.ist.psu.edu/

yamaguchi95synthesis.html [accessed 2008-06-15].[2273] Lidia A. R. Yamamoto and Christian F. Tschudin. Experiments on the automatic

evolution of protocols using genetic programming. In Autonomic Communication– Revised Selected Papers from the Second International IFIP Workshop, WAC2005, volume 3854/2006 of Lecture Notes in Computer Science (LNCS), pages 13–28. Springer Berlin / Heidelberg, October 2–5 2005, Vouliagmeni, Athens, Greece.ISBN: 978-3-54032-992-3. doi:10.1007/11687818 2. Online available at http://cn.

cs.unibas.ch/people/ly/doc/wac2005-lyct.pdf [accessed 2008-06-20]. See also [2274].[2274] Lidia A. R. Yamamoto and Christian F. Tschudin. Experiments on the automatic

evolution of protocols using genetic programming. Technical Report CS-2005-002,University of Basel, April 21, 2005. Online available at http://cn.cs.unibas.ch/

people/ly/doc/wac2005tr-lyct.pdf [accessed 2008-06-20]. See also [2273].[2275] Lidia A. R. Yamamoto and Christian F. Tschudin. Genetic evolution of protocol

implementations and configurations. In IFIP/IEEE International workshop on Self-Managed Systems and Services (SelfMan 2005), May 19, 2005, Nice, France. Onlineavailable at http://cn.cs.unibas.ch/pub/doc/2005-selfman.pdf [accessed 2007-09-17].

[2276] Lidia A. R. Yamamoto, Daniel Schreckling, and Thomas Meyer. Self-replicating andself-modifying programs in fraglets. In Proceedings of BIONETICS 2007, 2nd Inter-national Conference on Bio-Inspired Models of Network, Information, and ComputingSystems, 2007. In proceedings [1019]. Online available at http://cn.cs.unibas.ch/people/ly/doc/bionetics2007-ysm.pdf [accessed 2008-05-04].

[2277] Koetsu Yamazaki, Sourav Kundu, and Michitomo Hamano. Genetic program-ming based learning of control rules for variable geometry structures. In GeneticProgramming 1998: Proceedings of the Third Annual Conference, pages 412–415,1998. In proceedings [1209]. Online available at http://citeseer.ist.psu.edu/

kundu98genetic.html [accessed 2007-09-09].[2278] Hongmei Yan, Yingtao Jiang, Jun Zheng, Chenglin Peng, and Shouzhong Xiao. Dis-

covering critical diagnostic features for heart diseases with a hybrid genetic algorithm.In Faramarz Valafar and Homayoun Valafar, editors, Proceedings of the InternationalConference on Mathematics and Engineering Techniques in Medicine and BiologicalScienes, METMBS’03, pages 406–409. CSREA Press, June 2003, Las Vegas, Nevada,USA. ISBN: 1-9324-1504-1.

REFERENCES 789

[2279] Ang Yang, Yin Shan, and Lam Thu Bui, editors. Success in Evolu-tionary Computation, volume 92/2008 of Studies in Computational Intelli-gence. Springer Berlin / Heidelberg, January 16, 2008. ISBN: 3-5407-6285-X,978-3-54076-285-0, 978-3-54076-286-7. Library of Congress Control Number:2007939404. doi:10.1007/978-3-540-76286-7.

[2280] Shengxiang Yang, Yew-Soon Ong, and Yaochu Jin, editors. Evolutionary Computa-tion in Dynamic and Uncertain Environments, volume 51(XXIII) of Studies in Com-putational Intelligence. Springer, 2007. ISBN: 978-3-54049-772-1. Presentation onlineavailable at http://www.soft-computing.de/Jin_CEC04T.pdf.gz [accessed 2007-08-19].

[2281] Ziheng Yang and Joseph P. Bielawski. Statistical methods for detecting molecularadaptation. Trends in Ecology & Evolution, 15(12):496–503, December 1, 2000. ISSN:0169-5347. doi:10.1016/S0169-5347(00)01994-7. Online available at http://dx.

doi.org/10.1016/S0169-5347(00)01994-7 and http://citeseer.ist.psu.edu/

yang00statistical.html [accessed 2008-07-20].[2282] Xin Yao, editor. Progress in Evolutionary Computation, Selected Papers of the AI’93

and AI’94 Workshops on Evolutionary Computation Melbourne, Victoria, Australia,November 16, 1993 and Armidale, NSW, Australia, November 21-22, 1994, volume956/1995 of Lecture Notes in Artificial Intelligence, subseries of Lecture Notes inComputer Science (LNCS), May 1995. Springer, Berlin/Heidelberg, Germany. ISBN:3-5406-0154-6, 978-3-54060-154-8. doi:10.1007/3-540-60154-6.

[2283] Xin Yao. Optimization by genetic annealing. In M. Jabri, editor, Proceedings of Sec-ond Australian Conference on Neural Networks, pages 94–97, 1991, Sydney, Australia.Online available at http://citeseer.ist.psu.edu/yao91optimization.html andftp://www.cs.adfa.edu.au/pub/xin/acnn91.ps.Z [accessed 2007-09-10].

[2284] Xin Yao, editor. Evolutionary Computation: Theory and Applications. WorldScientific Publishing Co Pte Ltd, January 28, 1996. ISBN: 9-8102-2306-4,978-9-81022-306-9. Partly online available at http://books.google.de/books?

id=GP7ChxbJOE4C [accessed 2008-03-25].[2285] Xin Yao, Edmund K. Burke, Jose Antonio Lozano, Jim Smith, Juan J. Merelo

Guervos, John A. Bullinaria, Jonathan E. Rowe, Peter Tino, Ata Kaban, and Hans-Paul Schwefel, editors. Proceedings of the 8th International Conference on Paral-lel Problem Solving from Nature – PPSN VIII, volume 3242/2004 of Lecture Notesin Computer Science (LNCS), September 18–22, 2004, Birmingham, UK. Springer.ISBN: 3-5402-3092-0, 978-3-54023-092-2. doi:10.1007/b100601. See http://events.

cs.bham.ac.uk/ppsn04/ [accessed 2007-09-05].[2286] Xin Yao, F. Wang, K. Padmanabhan, and Sancho Salcedo-Sanz. Hybrid evolutionary

approaches to terminal assignment in communications networks. In Recent Advancesin Memetic Algorithms, pages 129–159. Springer, 2005. doi:10.1007/3-540-32363-5 7.In collection [901].

[2287] Maqsood Yaqub, Ronald Boellaard, Marc A Kropholler, and Adriaan A Lam-mertsma. Optimization algorithms and weighting factors for analysis of dy-namic pet studies. Physics in Medicine and Biology, 51:4217–4232, August 2006.doi:10.1088/0031-9155/51/17/007. Online available at stacks.iop.org/PMB/51/

4217 [accessed 2007-08-25].[2288] Frank Yates. The Design and Analysis of Factorial Experiments. Imperial Bureau of

Soil Science, Commonwealth Agricultural Bureaux, Harpenden, England, UK, 1937.ISBN: 0-8519-8220-4, 978-0-85198-220-5. ASIN: B00086SIZ0. Technical Communi-cation No. 35.

[2289] Tao Ye. Large-scale network parameter configuration using on-line simula-tion framework. PhD thesis, Department of Electrical, Computer and Sys-tem Engineering, Rensselaer Polytechnic Institute, Troy, New York 12180, USA,March 2003. Adviser: Shivkumar Kalyanaraman. Order-No. AAI3088530. Online

790 REFERENCES

available at http://www.ecse.rpi.edu/Homepages/shivkuma/research/papers/

tao-phd-thesis.pdf [accessed 2008-10-16].[2290] Tao Ye and Shivkumar Kalyanaraman. An adaptive random search alogrithm

for optimizing network protocol parameters. Technical Report, Department ofElectrical, Computer and System Engineering, Rensselaer Polytechnic Institute,Troy, New York 12180, USA, 2001. Online available at http://citeseerx.ist.

psu.edu/viewdoc/summary?doi=10.1.1.24.8138 and http://www.ecse.rpi.edu/

Homepages/shivkuma/research/papers/tao-icnp2001.pdf [accessed 2008-10-16].[2291] Gary G. Yen, Simon M. Lucas, Gary Fogel, Graham Kendall, Ralf Salomon, Byoung-

Tak Zhang, Carlos A. Coello Coello, and Thomas Philip Runarsson, editors. Pro-ceedings of the IEEE Congress on Evolutionary Computation, CEC 2006, July 16–21, 2006, Sheraton Vancouver Wall Centre Hotel, Vancouver, BC, Canada. IEEEPress, 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA. ISBN:0-7803-9487-9.

[2292] John Yen and Bogju Lee. A simplex genetic algorithm hybrid. In IEEE In-ternational Conference on Evolutionary Computation, pages 175–180, 1997. IEEEComputer Society, Washington, DC, USA. INSPEC Accession Number: 5573047.doi:10.1109/ICEC.1997.592291. In proceedings [106].

[2293] John Yen, James C. Liao, David Randolph, and Bogju Lee. A hybrid approachto modeling metabolic systems using genetic algorithms and the simplex method.In CAIA’95: Proceedings of the 11th Conference on Artificial Intelligence for Ap-plications, pages 277–285, 1995. IEEE Computer Society, Washington, DC, USA.ISBN: 0-8186-7070-3. doi:10.1109/CAIA.1995.378811. Online available at http://

citeseer.ist.psu.edu/33461.html [accessed 2008-07-23].[2294] John Yen, David Randolph, Bogju Lee, and James C. Liao. A hybrid genetic

algorithm for the identification of metabolic models. In TAI’95: Proceedings ofthe Seventh International Conference on Tools with Artificial Intelligence, pages4–7, November 5–8, 1995, Herndon, VA, USA. IEEE Computer Society, Wash-ington, DC, USA. ISBN: 0-8186-7312-5. INSPEC Accession Number: 5162198.doi:10.1109/TAI.1995.479371.

[2295] Gwoing Tina Yu and Peter Bentley. Methods to evolve legal phenotypes. In PPSNV: Proceedings of the 5th International Conference on Parallel Problem Solving fromNature, pages 280–291, 1998. doi:10.1007/BFb0056843. In proceedings [624]. Onlineavailable at http://www.cs.ucl.ac.uk/staff/p.bentley/YUBEC2.pdf [accessed 2007-

08-17].[2296] Gwoing Tina Yu and Julian Francis Miller. Neutrality and the evolvability of boolean

function landscape. In EuroGP’01: Proceedings of the 4th European Conference onGenetic Programming, pages 204–217, 2001. In proceedings [1423]. Online avail-able at http://citeseer.ist.psu.edu/yu01neutrality.html and http://www.

cs.mun.ca/~tinayu/index_files/addr/public_html/neutrality.pdf [accessed 2007-

11-03], see also http://www.cs.mun.ca/~tinayu/index_files/addr/public_html/

neutralityTalk.pdf [accessed 2007-11-03].[2297] Gwoing Tina Yu and Julian Francis Miller. Finding needles in haystacks is not hard

with neutrality. In EuroGP’02: Proceedings of the 5th European Conference on Ge-netic Programming, pages 13–25, 2002. In proceedings [737]. Online available athttp://citeseer.ist.psu.edu/yu02finding.html and http://www.cs.mun.ca/

~tinayu/index_files/addr/public_html/EuroGP2002.pdf [accessed 2007-11-02].[2298] Gwoing Tina Yu, Rick Riolo, and Bill Worzel, editors. Genetic Programming The-

ory and Practice III, Proceedings of the Genetic Programming Theory Practice 2005Workshop (GPTP-2005), volume 9 of Genetic Programming Series, May 12-14, 2005,The Center for the Study of Complex Systems (CSCS), University of Michigan, AnnArbor, Michigan, USA. Springer. ISBN: 978-0-38728-110-0. See http://www.cscs.

umich.edu/gptp-workshops/gptp2005/ [accessed 2007-09-28].

REFERENCES 791

[2299] Gwoing Tina Yu, Lawrence Davis, Cem M. Baydar, and Rajkumar Roy, ed-itors. Evolutionary Computation in Practice, volume 88/2008 of Studies inComputational Intelligence. Springer, January 2008. ISBN: 978-3-54075-770-2.doi:10.1007/978-3-540-75771-9. Series editor: Janusz Kacprzyk.

[2300] Tina Gwoing Yu. Program evolvability under environmental variationsand neutrality. In Advances in Artificial Life, pages 835–844, 2007.doi:10.1007/978-3-540-74913-4 84. In proceedings [614], see also [2301].

[2301] Tina Gwoing Yu. Program evolvability under environmental variations and neutral-ity. In Genetic and Evolutionary Computation Conference – Companion Material,GECCO 2007, pages 2973–2978, 2007. doi:10.1145/1274000.1274041. In proceedings[2038]: Workshop Session – Evolution of natural and artificial systems - metaphorsand analogies in single and multi-objective problems. Online available at http://

doi.acm.org/10.1145/1274000.1274041 [accessed 2009-02-20]. See also [2300].[2302] D. Yuret and M. Maza. A genetic algorithm system for predicting the oex. Technical

Analysis of Stocks & Commodities, pages 58–64, June 1994. Online available athttp://www.denizyuret.com/pub/tasc94.ps.gz and http://citeseer.ist.psu.

edu/yuret94genetic.html [accessed 2007-08-24].[2303] Deniz Yuret and Michael de la Maza. Dynamic hill climbing: Overcoming the limi-

tations of optimization techniques. In Proceedings of the Second Turkish Symposiumon Artificial Intelligence and Neural Networks, pages 208–212, June 24–25, 1993,Bogazici University, Istanbul, Turky. Online available at http://citeseer.ist.

psu.edu/yuret93dynamic.html and http://www.denizyuret.com/pub/tainn93.

html [accessed 2007-09-11].

Z

[2304] Vladimir Zakian. New formulation for the method of inequalities. Proceedings of theInstitution of Electrical Engineers, 126:579–584, 1979. See also [2305].

[2305] Vladimir Zakian. New formulation for the method of inequalities. In Madan G. Singh,editor, Systems and Control Encyclopedia, volume 5, pages 3206–3215. PergamonPress, New York, USA, 1987. ISBN: 978-0-08028-709-6, 0-0802-8709-3. See also[2304].

[2306] Vladimir Zakian. Perspectives on the principle of matching and the method of in-equalities. Control systems centre report 769, Control Systems Centre, Universityof Manchester Institute of Science and Technology (UMIST), P.O. Box 88, SackvilleStreet, Manchester M60 1QD, UK, 1992. See also [2307].

[2307] Vladimir Zakian. Perspectives on the principle of matching and the method of in-equalities. International Journal of Control, 65(1):147–175, September 1996. ISSN:0020-7179, 1366-5820. CODEN: IJCOAZ. doi:10.1080/00207179608921691. See also[2306].

[2308] Vladimir Zakian and U. AI-Naib. Design of dynamical and control systems by themethod of inequalities. IEE Proceedings D: Control Theory & Applications, 120(11):1421–1427, 1973. ISSN: 0143-7054.

[2309] A. M. S. Zalzala, editor. First International Conference on Genetic Algorithms in En-gineering Systems: Innovations and Applications (GALESIA), volume 414, Septem-ber 12–14, 1995, Scheffield, UK. IEE Conference Publication, Institution of Engineer-ing and Technology. ISBN: 978-0-85296-650-1.

[2310] Michael Zapf and Thomas Weise. Offline Emergence Engineering For Agent Soci-eties. In Proceedings of the Fifth European Workshop on Multi-Agent Systems (EU-MAS’07), December 14, 2007, Elmouradi Hotel, Hammamet, Tunesia. Also presentedat the co-located Fifth Technical Forum Group (TFG5). Online available at http://www.it-weise.de/documents/files/ZW2007EUMASTR.pdf [accessed 2009-06-26]. See also[2311].

792 REFERENCES

[2311] Michael Zapf and Thomas Weise. Offline Emergence Engineering For Agent So-cieties. Kasseler Informatikschriften (KIS) 2007, 8, University of Kassel, FB16,Distributed Systems Group, Wilhelmshoher Allee 73, 34121 Kassel, Germany, De-cember 7, 2007. Persistent Identifier: urn:nbn:de:hebis:34-2007120719844. On-line available at https://kobra.bibliothek.uni-kassel.de/handle/urn:nbn:

de:hebis:34-2007120719844 and http://www.it-weise.de/documents/files/

ZW2007EUMASTR.pdf [accessed 2007-11-20], see also [2310].[2312] Marvin Zelen and Norman C. Severo. Probability functions. In Milton Abramowitz

and Irene A. Stegun, editors, Handbook of Mathematical Functions with Formulas,Graphs, and Mathematical Tables, chapter 26. Dover Publications / National Bureauof Standards, first. (new ed june 1, 1965) edition, 1964. ISBN: 978-0-48661-272-0.

[2313] Guoli Zhang and Haiyan Lu. Hybrid real-coded genetic algorithm with quasi-simplextechnique. IJCSNS International Journal of Computer Science and Network Security,6(10):246–255, October 2006. ISSN: 1738-7906. Online available at http://paper.

ijcsns.org/07_book/200610/200610B15.pdf and [accessed 2008-07-29].[2314] Guoli Zhang, Hai Yan Lu, Gengyin Li, and Hong Xie. A new hybrid real-coded genetic

algorithm and application in dynamic economic dispatch. In WCICA 2006. The SixthWorld Congress on Intelligent Control and Automation, volume 1, pages 3627–3632,June 21–23, 2006, Dalian, China. ISBN: 1-4244-0332-4. INSPEC Accession Number:9187799. doi:10.1109/WCICA.2006.1713046.

[2315] P. Zhang and A.H. Coonick. Coordinated synthesis of pss parameters in multi-machine power systems using the method of inequalities applied to genetic algo-rithms. IEEE Transactions on Power Systems, 15(2):811–816, May 2000. Onlineavailable at http://www.lania.mx/~ccoello/EMOO/zhang00.pdf.gz and http://

citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.8.9768 [accessed 2008-11-14].[2316] Song Zhang and David H. Laidlaw. DTI fiber clustering and cross-subject cluster

analysis. In Proceedings International Society for Magnetic Resonance in Medicine(ISMRM), May 2005. Miami, FL. Online available at http://www.cs.brown.edu/

research/vis/docs/pdf/Zhang-2005-DFC.pdf [accessed 2007-08-11].[2317] Feng Zhao and Leonidas Guibas. Wireless Sensor Networks: An Information Pro-

cessing Approach. Morgan Kaufmann, July 6, 2004. ISBN: 978-1-55860-914-3.[2318] Jinghui Zhong, Xiaomin Hu, Jun Zhang, and Min Gu. Comparison of performance

between different selection strategies on simple genetic algorithms. In CIMCA’05:Proceedings of the International Conference on Computational Intelligence for Mod-elling, Control and Automation and International Conference on Intelligent Agents,Web Technologies and Internet Commerce Vol-2 (CIMCA-IAWTIC’06), pages 1115–1121, November 28–30, 2005. IEEE Computer Society, Washington, DC, USA. ISBN:076-9-52504-002-.

[2319] Chi Zhou, Peter C. Nelson, Weimin Xiao, and Thomas M. Tirpak. Discovery ofclassification rules by using gene expression programming. In Proceedings of theInternational Conference on Artificial Intelligence (IC-AI’02), pages 1355–1361, June2002. Las Vegas, U.S.A.

[2320] Chi Zhou, Weimin Xiao, Thomas M. Tirpak, and Peter C. Nelson. Evolving accurateand compact classification rules with gene expression programming. IEEE Transac-tions on Evolutionary Computation, 7(6):519–531, December 2003. ISSN: 1089-778X.

[2321] Wanlei Zhou and Andrzej Goscinski. An analysis of the web-based client-servercomputing models. In Proceedings of the Asia-Pacific Web Conference (APWeb’98),pages 343–348, September 1998, Beijing. Online available at http://citeseer.ist.psu.edu/296101.html [accessed 2007-08-13].

[2322] Jianping Zhu, Pete Bettinger, and Rongxia Li. Additional insight into the perfor-mance of a new heuristic for solving spatially constrained forest planning problems.Silva Fennica, 41(4):687–698, 2007. ISSN: 0037-5330. Online available at http://

www.metla.fi/silvafennica/full/sf41/sf414687.pdf [accessed 2008-08-23].

REFERENCES 793

[2323] Kenny Qili Zhu. A diversity-controlling adaptive genetic algorithm for the vehiclerouting problem with time windows. In 15th IEEE International Conference onTools with Artificial Intelligence (ICTAI’03), pages 176–183, November 3–5, 2003,Sacramento, California, USA. IEEE Computer Society, Washington, DC, USA. ISBN:0-7695-2038-3. INSPEC Accession Number: 7862120. doi:10.1109/TAI.2003.1250187.

[2324] Liming Zhu, Roger L. Wainwright, and Dale A. Schoenefeld. A genetic algorithmfor the point to multipoint routing problem with varying number of requests. InProceedings of the IEEE World Congress on Computational Intelligence, The 1998IEEE International Conference on Evolutionary Computation, pages 171–176, 1998.doi:10.1109/ICEC.1998.699496. In proceedings [1001]. Online available at http://

euler.mcs.utulsa.edu/~rogerw/papers/Zhu-ICEC98.pdf and http://citeseer.

ist.psu.edu/382506.html [accessed 2008-07-21].[2325] Karin Zielinski and Rainer Laur. Stopping criteria for constrained optimization with

particle swarms. In Proceedings of the Second International Conference on BioinspiredOptimization Methods and their Application, BIOMA 2006, pages 45–54, 2006. Inproceedings [669]. Extended version: [2326]. Online available at http://www.item.

uni-bremen.de/staff/zilli/zielinski06stopping_PSO.pdf [accessed 2007-09-13].[2326] Karin Zielinski and Rainer Laur. Stopping criteria for a constrained

single-objective particle swarm optimization algorithm. Informatica, 31(1):51–59, 2007. ISSN: Print: 0350-5596, Web: 1854-387. Extended versionof [2325]. Online available at http://www.item.uni-bremen.de/staff/zilli/

zielinski07informatica.pdf and http://www.informatica.si/vols/vol31.

html [accessed 2007-09-13].[2327] Anatas Zilinskas. Algorithm as 133: Optimization of one-dimensional multi-

modal functions. Applied Statistics, 27(3):367–375, 1978. ISSN: 00359254.doi:10.2307/2347182.

[2328] Stanley Zionts, editor. Proceedings of the 2nd International Conference on MultipleCriteria Decision Making (MCDM’1977), 1977, Buffalo, New York, USA.

[2329] Eckart Zitzler and Lothar Thiele. An evolutionary algorithm for multiobjectiveoptimization: The strength pareto approach. Technical Report 43, Computer En-gineering and Networks Laboratory (TIK), Swiss Federal Institute of TechnologyZurich (ETH), Gloriastrasse 35, CH-8092 Zurich, Switzerland, May 1998. Onlineavailable at http://www.tik.ee.ethz.ch/sop/publicationListFiles/zt1998a.

pdf and http://citeseer.ist.psu.edu/225338.html [accessed 2007-07-29].[2330] Eckart Zitzler, Kalyanmoy Deb, and Lothar Thiele. Comparison of multiobjec-

tive evolutionary algorithms: Empirical results. Evolutionary Computation, 8(2):173–195, 2000. Online available at http://sci2s.ugr.es/docencia/cursoMieres/EC-2000-Comparison.pdf and http://citeseer.comp.nus.edu.sg/362080.html

[accessed 2008-04-06].[2331] Eckart Zitzler, Kalyanmoy Deb, Lothar Thiele, Carlos A. Coello Coello, and David

Corne, editors. Evolutionary Multi-Criterion Optimization, Proceedings of the FirstInternational Conference on Evolutionary Multi-Criterion Optimization (EMO’01),volume 1993/2001 of Lecture Notes in Computer Science (LNCS), March 7–9, 2001,Zurich, Switzerland. Springer-Verlag, Berlin. ISBN: 3-5404-1745-1. See http://www.

tik.ee.ethz.ch/emo/ [accessed 2007-09-11].[2332] Eckart Zitzler, Marco Laumanns, and Lothar Thiele. SPEA2: Improving the

Strength Pareto Evolutionary Algorithm. Technical Report 103, Computer En-gineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology(ETH) Zurich, Gloriastrasse 35, CH-8092 Zurich, Switzerland, May 2001. Onlineavailable at http://www.tik.ee.ethz.ch/sop/publicationListFiles/zlt2001a.pdf and http://citeseer.ist.psu.edu/514031.html [accessed 2007-07-29]. Errata added2001-09-27.

794 REFERENCES

[2333] Constantin Zopounidis, editor. Proceedings of the 18th International Conferenceon Multiple Criteria Decision Making (MCDM’2006), June 9–13, 2006, MAICh(Mediterranean Agronomic Institute of Chania) Conference Centre, Chania, Crete,Greece. See http://www.dpem.tuc.gr/fel/mcdm2006/ [accessed 2007-09-10].

[2334] Jie Zuo, Changjie Tang, and Tianqing Zhang. Mining predicate association ruleby gene expression programming. In Xiaofeng Meng, Jianwen Su, and Yujun Wang,editors, WAIM’02: Proceedings of the Third International Conference on Advances inWeb-Age Information Management, volume 2419/2002 of Lecture Notes in ComputerScience (LNCS), pages 281–294. Springer-Verlag, August 11–13, 2002, Beijing, China.ISBN: 3-5404-4045-3. Computer Department, Sichuan University China.

[2335] Katharina Anna Zweig. On Local Behavior and Global Structures in the Evo-lution of Complex Networks. PhD thesis, Fakultat fur Informations- und Kog-nitionswissenschaften der Eberhard-Karls-Universitat Tubingen, July 2007. On-line available at http://www-pr.informatik.uni-tuebingen.de/mitarbeiter/

katharinazweig/downloads/Diss.pdf [accessed 2008-06-12]. Zweig was formerly knownas Lehmann.

Index

(1 + 1)− ES, 228(GE)2, 184(µ′, λ′(µ, λ)γ)-ES, 229(µ+ 1)-ES, 228(µ+ λ), 231(µ+ λ)-ES, 228(µ, λ)-ES, 229(µ/ρ+ λ)-ES, 229(µ/ρ, λ)-ES, 229χ2 Distribution, 49015-rule, 229

(µ+ λ), 102(µ, λ), 102µGP, 192σ-algebra, 469τ -EO, 270Γ , 532Θ notation, 551Fraglets, 216, 404

evolution of, 40480x86, 193

A⋆ Search, 296AAAI, 87Abstraction, 549ACO, 245Acquisition

module, 70, 202Action, 238AdaBoost, 383Adaptive Grammar, 568Adaptive Walk, 297

fitter dynamics, 297greedy dynamics, 297one-mutant, 297

ADDO, 392

Adenine, 42Adequacy

functional, 222ADF, 167, 168, 193, 196ADG, 199Adjacency, 44, 45adjacent neighbors, 330Adjunction, 570ADL, 198ADM, 168Admissible, 296AG, 565, 566AGA, 310Agents, 105, 160, 231, 254Aggregate, 415Aggregate Function, 415Aggregating

linear, 29Aggregation, 414, 415

gossip-based, 415linear, 29proactive, 415reactive, 415

Aggregation Protocols, 414, 415AI, 373AIM-GP, 193AIMGP, 193–195AISB, 88AL, 213Algorithm, 547

abstraction, 549anytime, 222complexity, 550determined, 550determinism, 550deterministic, 552

795

796 INDEX

discrete, 549distributed, 553euclidean, 357evaluate, 219evolutionary, 95evolve, 219finite, 550generational, 102Las Vegas, 552Monte Carlo, 552optimization, 48

baysian, 70probabilistic, 22, 552randomized, 552termination, 550

Algorithmic Chemistry, 204ALife, 213All-Or-Nothing, 223, 404Allele, 43Alphabet, 562ANN, 197, 374Ant

artificial, 27Ant Colony Optimization, 245Antisymmetrie, 463ANTS, 246Anytime Algorithm, 222appleJuice, 558Application Server, 557Applications, 315Architecture

service oriented, 383Artificial Ant, 27, 354Artificial Chemistry, 213Artificial Embryogeny, 155Artificial Life, 213Asexual Reproduction, 145Assignment

soft, 206Assimilation

genetic, 279Asymmetrie, 463Attribute, 565

inherited, 565synthesized, 565

Attribute Grammar, 565extended, 567, 568L-attributed, 566reflective, 185S-attributed, 567

Autoconstructive Evolution, 215Autocorrelation, 63Automatically Defined Functions, 167, 196Automatically Defined Groups, 199Automatically Defined Link, 198Automaton

cellular, 160, 231Auxiliary Tree, 570

Average, 416

Backus-Naur Form, 564extended, 565

Baldwin, 278effect, 278

Battery, 559Bayes Classifier

naıve, 374Bayesian Optimization Algorithm, 70BBH, 152bcGP, 194BDP, 304Bee, 235Bernoulli

distribution, 483experiment, 483trial, 483

Best-First Search, 295BFS, 291BGP, 172Bias, 499Bibtex, 591Big-Ω notation, 551Big-O notation, 550Bijective, 462Bijectivity, 462BinInt, 58, 59, 337, 338Binomial Distribution, 483, 511Biochemistry, 87, 160, 231, 274, 280, 284Biology, 87, 105, 261BIOMA, 105, 246BIONETS, 404Bird, 235BitCount, 337Bittorrent, 558Black-Box, 23Bloat, 66Block

building, 152BLUE, 503Bluetooth, 559BNF, 564BOA, 70Boosting, 383Bottleneck, 554Box-Muller, 529

polar, 529BPEL, 395BPEL4WS, 393Breadth-First Search, 291Broadcast-Distributed Parallel Evolutionary Al-

gorithm, 304BTNodes, 560Bucket Brigade, 240Building Block, 152Building Block Hypothesis, 152, 334Bus, 555Bypass

INDEX 797

extradimensional, 85

C, 193, 194CACSD, 34Candidate

solution, 42Cartesian Genetic Programming, 67, 199, 201,

202embedded, 201

Catastrophecomplexity, 331

Causality, 62, 83, 346CDF, 470

continous, 471discrete, 471

CEC, 105, 246Cellular Automaton, 160, 231Cellular Encoding, 174Central Limit Theorem, 489Central Point Of Failure, 554Centroid, 537CFG, 563, 564CGE, 186CGP, 67, 199–202

embedded, 201CGPS, 193Change

non-synonymous, 66synonymous, 66

Character String, 562Checking

model, 221Chemical Engineering, 87, 105, 142, 227, 230,

231, 251, 265, 284Chemistry, 87, 105, 142, 227, 230, 231, 251, 265,

284algorithmic, 204artificial, 213

Chi-square Distribution, 490Chomsky Hierarchy, 563Christiansen

grammar, 186, 569Christiansen Grammar, 186

evolution, 186Christiansen Grammars, 569Chromosome, 145Chromosomes

stringfixed-length, 146variable-length, 148

tree, 162CI, 109, 503CISC, 193Class

equivalence, 464Classifier, 238Classifier Systems, 233, 234, 378, 445

learning, 233, 239, 374non-learning, 239

Clearing, 134Client, 556Client-Server, 301, 556Closure, 178, 226CLT, 489Clustering, 535k-means, 540nth nearest neighbor, 541algorithm, 536hierarchical, 535leader, 543linkage, 541partitional, 535, 540partitions, 536square error, 540

Co-Evolution, 269Code Bloat, 399Codons, 172Coefficient

negative slope, 63Coefficient of Variation, 475Combination, 467Combinatorics, 467Communication, 87, 105, 142, 160, 246, 254, 265,

271, 274, 280, 291Completeness, 44, 290

weak, 44Complexity Catastrophe, 331Compress, 201Computational Embryogeny, 155Computational Intelligence, 109Computer

sciencetheoretical, 547

Computingamorphous, 413ubiquitous, 411, 413

Concatenation, 562Condition, 236Confidence

coefficient, 504interval, 503

Connection Register, 205Content Sharing, 558Contest, 373Continuous Distributions, 484Contravariance, 386Convergence

domino, 58, 59, 85, 338premature, 58prevention, 136

Correlationfitness distance, 62genotype-fitness, 63operator, 62

Count, 472Covariance, 386, 475CPU, 193, 206

798 INDEX

Creation, 137, 146, 148, 162, 197Credit Assignment Problem, 239Criterion

termination, 54Criticality

self-organized, 269Crossbow, 560Crossover, 98, 138, 147–149, 164

homologous, 148, 195, 404point, 147SAAN, 198simplex, 287single-point, 147, 165SSAAN, 198SSIAN, 198sticky, 195strong context preserving, 165tree, 164

CS, 233CSG, 563CSP, 87, 105Cumulative Distribution Function, 470Cut, 153Cut & Splice, 148Cytosine, 42

Dagstuhl Seminar, 106Data Mining, 105, 142, 160, 174, 227, 231, 233,

254, 284, 373, 535DATA-MINING-CUP, 373, 374Database Server, 557DE, 229, 230, 286Death Penalty, 34Deceptiveness, 63, 69, 333Deceptivity, 63, 69Decile, 478Decision Maker

external, 37Decision Tree, 374Decreasing, 463

monotonically, 463Default Hierarchy, 238, 378Defense, 105Defined Length, 150DELB, 230Deme, 301Density Estimation, 506

crowding distance, 507Kernel, 508nearest neighbor, 506Parzen window, 508

Deoxyribonucleic acid, 42Deoxyribose, 42Depth-First Search, 292

iterative deepenining, 294Depth-limited Search, 293Derivation Tree, 563DERL, 230DES, 229

Design, 87, 105, 230, 271, 280circuit, 105, 142, 160, 174, 202, 230, 231, 251,

265Detector, 234Determined, 550Determinism, 550DFS, 292Differential Evolution, 229, 230, 286Differential Evolution Strategy, 229Discrete, 549Discrete Distributions, 479Distance

Euclidian, 538Hamming, 537Manhattan, 537Measure, 537

Distributed algorithms, 553Distribution, 299, 470, 479, 484χ2, 490Binomial, 483, 511chi-square, 490continuous, 484discrete, 479exponential, 489, 530normal, 486, 529

multivariate, 488standard, 486

Poisson, 480Student’s t, 494t, 494uniform, 479, 485, 527, 529, 530

continuous, 485discrete, 479

Diversification, 60Diversity, 59, 226DMC, 373DNA, 42, 172, 195do not Care, 378, 382DoE, 317Domination, 31Domino

convergence, 58, 59, 85, 338Don’t Care, 150, 236Downhill Simplex, 283DPE, 338Drunkyard’s Walk, 294Duplication, 137Dust Networks, 560

E-code, 145EA, 95, 101, 105, 108–110, 414EA/AE, 106EAG, 567, 568EARL, 233EBNF, 565ECGP, 201ECJ, 186Economics, 87, 105, 142, 160, 184, 265Edge Encoding, 174

INDEX 799

EDI, 195Editing, 165EDL, 570Effect

Baldwin, 278hiding, 279

Effector, 234Efficiency

Pareto, 31home, 558Elitism, 103Embrogeny, 154

artificial, 154Embryogenesis, 154Embryogenic, 154Embryogeny

artificial, 155computational, 155

EMO, 106EMOO, 96, 109Encapsulation, 166Encoding

cellular, 174edge, 174

Endnote, 591Energy Source, 559Engineering, 87, 105, 230, 271, 280

electrical, 105, 142, 160, 174, 202, 230, 231,251, 265

Entropy, 478continuous, 478differential, 478information, 478

Entscheidungsproblem, 220Environment, 234

protection, 105, 227surveillance, 105, 227

EO, 269, 271Eoarchean, 97EP, 101, 231, 232Ephemeral Random Constants, 398Epistacy, 68Epistasis, 63, 68, 344, 352, 353

in Genetic Programming, 202in GPMs, 204positional, 203semantic, 202

Epistatic Road, 336Epistatic Variance, 63Equilibrium, 269

punctuated, 65, 269Equivalence

class, 464relation, 464

eRBGP, 211, 212ERL, 233Error, 499α, 509

β, 509mean square, 499threshold, 62, 338type 1, 509type 2, 509

Error Threshold, 62, 338ES, 100, 227, 228Estimation Theory, 499Estimator, 499

best linear unbiased, 503maximum likelihood, 502point, 499unbiased, 499

Euclidean Algorithm, 357Euclidian Distance, 538EUROGEN, 106, 143, 228, 232EuroGP, 160Evaluation, 53Event

certain, 467conflicting, 467elementary, 466impossible, 467random, 466

EvoCOP, 106Evolution

autoconstructive, 215Baldwinian, 278differential, 229Lamarckian, 278

Evolution Strategy, 100, 227, 228Evolutionary Algorithm, 95, 105, 108–110

basic, 98broadcast-distributed parallel, 304cycle, 96generational, 102multi-objective, 96parallelization, 300steady state, 102

Evolutionary Operation, 101, 283randomized, 101

Evolutionary Programming, 101, 201, 231, 232Evolutionary Reinforcement Learning, 233Evolvability, 62, 65EVOP, 101EvoWeb, 402EvoWorkshops, 107Expand, 201expand, 289Expected value, 473Experiment

design of, 317factorial, 317

Exploitation, 60, 62Exploration, 60, 289Exponential Distribution, 489Extended Backus-Naur Form, 565External Decision Maker, 37

800 INDEX

Extinctive Selection, 102left, 102right, 102

Extradimensional Bypass, 85Extrema Selection, 67Extremal Optimization, 269–271

generalized, 270

Factorial, 467False Negative, 509False Positive, 509FDC, 62FDL, 575FEA, 107Fibonacci Path, 341File Sharing, 558Finance, 87, 105, 142, 160, 184, 265Finite, 550Finite State Machine, 158, 231, 355Fisher’s Exact Test, 524Fitness, 46

nature, 100optimization, 100

Fitness Assignment, 111Pareto ranking, 112Prevalence ranking, 112Tournament, 120weighted sum, 112

Fitness Landscape, 47deceptive, 63ND, 333neutral, 64NK, 329NKp, 332NKq, 332p-Spin, 332rugged, 61technological, 332

Fly, 234FOCI, 107FOGA, 143home, 558Forma, 62, 80, 81

analysis, 80Formae, 81Formal Grammar, 563Free Lunch

no, 76Frequency

absolute, 468relative, 468

Frog, 234FSM, 160, 231Full, 163Fully Connected, 556Function, 462

ADF, 167, 196aggregate, 415automatically defined, 167, 196

benchmark, 327cumulative distribution, 470gamma, 532monotone, 463objective, 21penalty, 34

adaptive, 34dynamic, 34

probability density, 472probability mass, 471synthesis, 160, 174, 191, 202trap, 64, 333zeta, 532

Functional, 462Functionality, 462FWGA, 143

G3P, 177GA, 100, 141–144

messy, 152Gads, 179–181, 185, 204

1, 1792, 185

GAGS, 179GALESIA, 143Game, 105, 160, 231, 254Gamma, 532Gamma System, 216Gauss-Markov Theorem, 503GCD, 357, 358

problem, 357GCL, 207GE, 181, 182, 184, 204GECCO, 107, 143, 161, 246, 251GEM, 108, 143Gene, 43Gene Expression Programming, 172, 174Generality, 74Generation, 53Generational, 102Generative Grammar, 562Genetic Algorithm, 100, 141–144, 242, 287

cellular, 305cycle, 141for deriving software, 179grammar-based, 179messy, 70, 152

Genetic Algorithms, 158natural representation, 145real-encoded, 145

Genetic Assimilation, 279Genetic Network Programming, 199Genetic Programming, 100, 157, 160–162

binary, 172byte code, 194compiling system, 193crossover

homologous, 195epistasis, 202