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Genetic Programming: An Introduction and Tutorial, with a Survey of Techniques and Applications

  • William B. Langdon
  • Riccardo Poli
  • Nicholas F. McPhee
  • John R. Koza
Part of the Studies in Computational Intelligence book series (SCI, volume 115)

The goal of having computers automatically solve problems is central to artificial intelligence, machine learning, and the broad area encompassed by what Turing called ‘machine intelligence’ [384]. Machine learning pioneer Arthur Samuel, in his 1983 talk entitled ‘AI: Where It Has Been and Where It Is Going’ [337], stated that the main goal of the fields of machine learning and artificial intelligence is:

“to get machines to exhibit behavior, which if done by humans, would be assumed to involve the use of intelligence.”

Keywords

Genetic Programming Machine Code Symbolic Regression Linear Genetic Programming Technical Trading Rule 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Al-Sakran SH, Koza JR, Jones LW (2005) Automated re-invention of a pre-viously patented optical lens system using genetic programming. In: Keijzer M, Tettamanzi A, Collet P, van Hemert JI, Tomassini M (eds) Proceed-ings of the 8th European Conference on Genetic Programming, Springer, Lausanne, Switzerland, Lecture Notes in Computer Science, vol 3447, pp 25-37, URL http://springerlink.metapress.com/openurl.asp?genre=article&i%ssn=0302-9743&volume=3447&spage=25 Google Scholar
  2. 2.
    Allen J, Davey HM, Broadhurst D, Heald JK, Rowland JJ, Oliver SG, Kell DB (2003) High-throughput classification of yeast mutants for functional genomics using metabolic footprinting. Nature Biotechnology 21(6):692-696, DOI doi:10. 1038/nbt823, URL http://dbkgroup.org/Papers/NatureBiotechnology21(692-696).pdf Google Scholar
  3. 3.
    Altenberg L (1994) Emergent phenomena in genetic programming. In: Sebald AV, Fogel LJ (eds) Evolutionary Programming—Proceedings of the Third Annual Conference, World Scientific Publishing, San Diego, CA, USA, pp 233-241, URL http://dynamics.org/˜altenber/PAPERS/EPIGP/ Google Scholar
  4. 4.
    Alves da Silva AP, Abrao PJ (2002) Applications of evolutionary computation in electric power systems. In: Fogel DB, El-Sharkawi MA, Yao X, Greenwood G, Iba H, Marrow P, Shackleton M (eds) Proceedings of the 2002 Congress on Evolutionary Computation CEC2002, IEEE Press, pp 1057-1062, DOI doi: 10.1109/CEC.2002.1004389Google Scholar
  5. 5.
    Ando D, Dahlsted P, Nordahl M, Iba H (2007) Interactive GP with tree rep-resentation of classical music pieces. In: Giacobini M, Brabazon A, Cagnoni S, Di Caro GA, Drechsler R, Farooq M, Fink A, Lutton E, Machado P, Minner S, O’Neill M, Romero J, Rothlauf F, Squillero G, Takagi H, Uyar AS, Yang S (eds) Applications of Evolutionary Computing, EvoWorkshops 2007: EvoCOMNET, EvoFIN, EvoIASP, EvoInteraction, EvoMUSART, EvoSTOC, EvoTransLog, Springer Verlag, Valencia, Spain, LNCS, vol 4448, pp 577-584, DOI doi:10.1007/978- 3- 540- 71805- 5 63Google Scholar
  6. 6.
    Andre D, Koza JR 1996 Parallel genetic programming: A scalable implemen-tation using the transputer network architecture. In: Angeline PJ, Kinnear, Jr KE (eds) Advances in Genetic Programming 2, MIT Press, Cambridge, MA, USA, chap 16, pp 317-338Google Scholar
  7. 7.
    Andre D, Koza JR (1998) A parallel implementation of genetic programming that achieves super-linear performance. Information Sciences 106(3-4):201-218, URL http://www.sciencedirect.com/science/article/B6V0C-3TKS65B-21/2/22b9842f820b08883990bbae1d889c03 Google Scholar
  8. 8.
    Andre D, Bennett III FH, Koza JR (1996) Discovery by genetic program-ming of a cellular automata rule that is better than any known rule for the majority classification problem. In: Koza JR, Goldberg DE, Fogel DB, Riolo RL (eds) Genetic Programming 1996: Proceedings of the First Annual Conference, MIT Press, Stanford University, CA, USA, pp 3-11, URL http://www.genetic-programming.com/jkpdf/gp1996gkl.pdf Google Scholar
  9. 9.
    Angeline PJ (1996) An investigation into the sensitivity of genetic program-ming to the frequency of leaf selection during subtree crossover. In: Koza JR, Goldberg DE, Fogel DB, Riolo RL (eds) Genetic Programming 1996: Proceed-ings of the First Annual Conference, MIT Press, Stanford University, CA, USA, pp 21-29, URL http://www.natural-selection.com/Library/1996/gp96.zip Google Scholar
  10. 10.
    Angeline PJ 1997 Subtree crossover: Building block engine or macromuta-tion? In: Koza JR, Deb K, Dorigo M, Fogel DB, Garzon M, Iba H, Riolo RL (eds) Genetic Programming 1997: Proceedings of the Second Annual Conference, Morgan Kaufmann, Stanford University, CA, USA, pp 9-17Google Scholar
  11. 11.
    Angeline PJ (1998) Multiple interacting programs: A representation for evolving complex behaviors. Cybernetics and Systems 29(8):779-806, URL http://www.natural-selection.com/Library/1998/mips3.pdf zbMATHGoogle Scholar
  12. 12.
    Angeline PJ, Kinnear, Jr KE (eds) (1996) Advances in Genetic Programming 2. MIT Press, Cambridge, MA, USA, URL http://www.cs.bham.ac.uk/˜wbl/aigp2.html Google Scholar
  13. 13.
    Angeline PJ, Pollack JB (1992) The evolutionary induction of subroutines. In: Proceedings of the Fourteenth Annual Conference of the Cognitive Science Society, Lawrence Erlbaum, Bloomington, Indiana, USA, pp 236-241, URL http://www.demo.cs.brandeis.edu/papers/glib92.pdf
  14. 14.
    Arkov V, Evans C, Fleming PJ, Hill DC, Norton JP, Pratt I, Rees D, Rodriguez-Vazquez K(2000) System identification strategies applied to aircraft gas turbine engines. Annual Reviews in Control 24(1):67-81, URL http://www.sciencedirect.com/science/article/B6V0H-482MDPD-8/2/dd470648e2228c84efe7e14ca3841b7e Google Scholar
  15. 15.
    Austin MP, Bates G, Dempster MAH, Leemans V, Williams SN (2004) Adaptive systems for foreign exchange trading. Quantitative Finance 4(4):37-45, DOI doi:10.1080/14697680400008593, URL http://www-cfr.jbs.cam.ac.uk/archive/PRESENTATIONS/seminars/2006/dempster2.pdf Google Scholar
  16. 16.
    Azaria Y, Sipper M (2005a) GP-gammon: Genetically programming backgam-mon players. Genetic Programming and Evolvable Machines 6(3):283-300, DOI doi:10.1007/s10710- 005- 2990- 0, URL http://www.cs.bgu.ac.il/˜sipper/papabs/gpgammon.pdf, published online: 12 August 2005Google Scholar
  17. 17.
    Azaria Y, Sipper M (2005b) Using GP-gammon: Using genetic programming to evolve backgammon players. In: Keijzer M, Tettamanzi A, Collet P, van Hemert JI, Tomassini M (eds) Proceedings of the 8th European Conference on Genetic Programming, Springer, Lausanne, Switzerland, Lecture Notes in Computer Science, vol 3447, pp 132-142, URL http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=3447&spage=132 Google Scholar
  18. 18.
    Babovic V (1996) Emergence, evolution, intelligence; Hydroinformatics - A study of distributed and decentralised computing using intelligent agents. A. A. Balkema Publishers, Rotterdam, HollandGoogle Scholar
  19. 19.
    Bader-El-Den M, Poli R (2007a) Generating sat local-search heuristics using a gp hyper-heuristic framework. In: Proceedings of Evolution ArtificielleGoogle Scholar
  20. 20.
    Bader-El-Den MB, Poli R (2007b) A GP-based hyper-heuristic framework for evolving 3-SAT heuristics. In: Thierens D, Beyer HG, Bongard J, Branke J, Clark JA, Cliff D, Congdon CB, Deb K, Doerr B, Kovacs T, Kumar S, Miller JF, Moore J, Neumann F, Pelikan M, Poli R, Sastry K, Stanley KO, Stut-zle T, Watson RA, Wegener I (eds) GECCO ’07: Proceedings of the 9th annual conference on Genetic and evolutionary computation, ACM Press, London, vol 2, pp 1749-1749, URL http://www.cs.bham.ac.uk/˜wbl/biblio/gecco2007/docs/p1749.pdf Google Scholar
  21. 21.
    Bains W, Gilbert R, Sviridenko L, Gascon JM, Scoffin R, Birchall K, Har-vey I, Caldwell J 2002 Evolutionary computational methods to predict oral bioavailability QSPRs. Current Opinion in Drug Discovery and Development 5 (1):44-51Google Scholar
  22. 22.
    Baker JE 1987 Reducing bias and inefficiency in the selection algorithm. In: Grefenstette JJ (ed) Proceedings of the Second International Conference on Genetic Algorithms and their Application, Lawrence Erlbaum Associates, Cambridge, MA, USA, pp 14-21Google Scholar
  23. 23.
    Balic J(1999) Flexible Manufacturing Systems; Development - Structure - Operation- Handling- Tooling. Manufacturing technology, DAAAM International, ViennaGoogle Scholar
  24. 24.
    Banzhaf W (1993) Genetic programming for pedestrians. In: Forrest S (ed) Pro-ceedings of the 5th International Conference on Genetic Algorithms, ICGA-93, Morgan Kaufmann, University of Illinois at Urbana-Champaign, p 628, URL http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/ftp.io.com/papers/GenProg_forPed.ps.Z
  25. 25.
    Banzhaf W, Nordin P, Keller RE, Francone FD 1998 Genetic Programming - An Introduction; On the Automatic Evolution of Computer Programs and its Applications. Morgan Kaufmann, San Francisco, CA, USAzbMATHGoogle Scholar
  26. 26.
    Barrett SJ (2003) Recurring analytical problems within drug discovery and development. In: Scheffer T, Leser U (eds) Data Mining and Text Mining for Bioinformatics: Proceedings of the European Workshop, Dubrovnik, Croatia, pp 6-7, URL http://www2.informatik.hu-berlin.de/˜scheffer/publications/ProceedingsWS2003.pdf, invited talkGoogle Scholar
  27. 27.
    Barrett SJ, Langdon WB (2006) Advances in the application of machine learning techniques in drug discovery, design and development. In: Tiwari A, Knowles J, Avineri E, Dahal K, Roy R (eds) Applications of Soft Computing: Recent Trends, Springer, On the World Wide Web, Advances in Soft Comput-ing, pp 99-110, URL http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/barrett_2005_WSC.pdf
  28. 28.
    Bennett III FH (1996) Automatic creation of an efficient multi-agent architecture using genetic programming with architecture-altering operations. In: Koza JR, Goldberg DE, Fogel DB, Riolo RL (eds) Genetic Programming 1996: Proceedings of the First Annual Conference, MIT Press, Stan-ford University, CA, USA, pp 30-38, URL http://cognet.mit.edu/library/books/view?isbn=0262611279 Google Scholar
  29. 29.
    Bennett III FH, Koza JR, Shipman J, Stiffelman O (1999) Building a par-allel computer system for $18,000 that performs a half peta-flop per day. In: Banzhaf W, Daida J, Eiben AE, Garzon MH, Honavar V, Jakiela M, Smith RE (eds) Proceedings of the Genetic and Evolutionary Computation Confer-ence, Morgan Kaufmann, Orlando, Florida, USA, vol 2, pp 1484-1490, URL http://www.genetic-programming.com/jkpdf/gecco1999beowulf.pdf Google Scholar
  30. 30.
    Bhanu B, Lin Y, Krawiec K (2005) Evolutionary Synthesis of Pattern Recog-nition Systems. Monographs in Computer Science, Springer-Verlag, New York, URL http://www.springer.com/west/home/computer/imaging?SGWID=4-14%9-22-39144807-detailsPage=ppmmedia—aboutThisBook Google Scholar
  31. 31.
    Blickle T (1996) Theory of evolutionary algorithms and application to sys-tem synthesis. PhD thesis, Swiss Federal Institute of Technology, Zurich, URL http://www.handshake.de/user/blickle/publications/diss.pdf Google Scholar
  32. 32.
    Brabazon A, O’Neill M 2006 Biologically Inspired Algorithms for Financial Modeling. Natural Computing Series, SpringerGoogle Scholar
  33. 33.
    Brameier M, Banzhaf W (2001) A comparison of linear genetic programming and neural networks in medical data mining. IEEE Transactions on Evolutionary Computation 5(1):17-26, URL http://web.cs.mun.ca/˜banzhaf/papers/ieee_taec.pdf Google Scholar
  34. 34.
    Brameier M, Banzhaf W (2007) Linear Genetic Programming. No. XVI in Genetic and Evolutionary Computation, Springer, URL http://www.springer. com/west/home/default?SGWID=4-40356-22-173660820-0
  35. 35.
    Brameier M, Haan J, Krings A, MacCallum RM (2006) Automatic discovery of cross-family sequence features associated with protein function. BMC bioin-formatics [electronic resource] 7(16), DOI doi:10.1186/1471- 2105- 7- 16, URL http://www.biomedcentral.com/content/pdf/1471-2105-7-16.pdf
  36. 36.
    Brave S (1996) Evolving recursive programs for tree search. In: Angeline PJ, Kinnear, Jr KE (eds) Advances in Genetic Programming 2, MIT Press, Cambridge, MA, USA, chap 10, pp 203-220Google Scholar
  37. 37.
    Brezocnik M(2000) Uporaba genetskega programiranja v inteligentnih proizvodnih sistemih. University of Maribor, Faculty of mechanical engineering, Maribor, Slovenia, URL http://maja.uni-mb.si/slo/Knjige/2000-03-mon/index.htm Google Scholar
  38. 38.
    Brezocnik M, Balic J, Gusel L 2000 Artificial intelligence approach to determination of flow curve. Journal for technology of plasticity 25(1-2):1-7Google Scholar
  39. 39.
    Buason G, Bergfeldt N, Ziemke T (2005) Brains, bodies, and beyond: Competitive co-evolution of robot controllers, morphologies and environments. Genetic Programming and Evolvable Machines 6(1):25-51, DOI doi:10.1007/s10710- 005- 7618-xGoogle Scholar
  40. 40.
    Burke E, Kendall G, Newall J, Hart E, Ross P, Schulenburg S (2003) Hyper-heuristics: an emerging direction in modern search technology. In: Glover F, Kochenberger G (eds) Handbook of Metaheuristics, Kluwer Academic Publishers, pp 457-474Google Scholar
  41. 41.
    Burke EK, Hyde MR, Kendall G (2006) Evolving bin packing heuristics with genetic programming. In: Runarsson TP, Beyer HG, Burke E, Merelo-Guervos JJ, Whitley LD, Yao X (eds) Parallel Problem Solving from Nature - PPSN IX, Springer-Verlag, Reykjavik, Iceland, LNCS, vol 4193, pp 860-869, DOI doi: 10.1007/11844297 87, URL http://www.cs.nott.ac.uk/˜mvh/ppsn2006.pdf Google Scholar
  42. 42.
    Burke EK, Hyde MR, Kendall G, Woodward J (2007) Automatic heuristic gen-eration with genetic programming: evolving a jack-of-all-trades or a master of one. In: Thierens D, Beyer HG, Bongard J, Branke J, Clark JA, Cliff D, Con-gdon CB, Deb K, Doerr B, Kovacs T, Kumar S, Miller JF, Moore J, Neumann F, Pelikan M, Poli R, Sastry K, Stanley KO, Stutzle T, Watson RA, Wegener I (eds) GECCO ’07: Proceedings of the 9th annual conference on Genetic and evolutionary computation, ACM Press, London, vol 2, pp 1559-1565, URL http://www.cs.bham.ac.uk/wbl/biblio/gecco2007/docs/p1559.pdf Google Scholar
  43. 43.
    Buxton BF, Langdon WB, Barrett SJ (2001) Data fusion by intelligent clas-sifier combination. Measurement and Control 34(8):229-234, URL http://www.cs.ucl.ac.uk/staff/W.Langdon/mc/ Google Scholar
  44. 44.
    Cagnoni S, Bergenti F, Mordonini M, Adorni G (2005) Evolving binary classifiers through parallel computation of multiple fitness cases. IEEE Trans-actions on Systems, Man and Cybernetics - Part B 35(3):548-555, DOI doi: 10.1109/TSMCB.2005.846671Google Scholar
  45. 45.
    Cai W, Pacheco-Vega A, Sen M, Yang KT (2006) Heat transfer correlations by symbolic regression. International Journal of Heat and Mass Transfer 49(23-24):4352-4359, DOI doi:10.1016/j.ijheatmasstransfer.2006.04.029zbMATHGoogle Scholar
  46. 46.
    Castillo F, Kordon A, Smits G 2006 Robust pareto front genetic programming parameter selection based on design of experiments and industrial data. In: Riolo RL, Soule T, Worzel B (eds) Genetic Programming Theory and Practice IV, Genetic and Evolutionary Computation, vol 5, Springer, Ann ArborGoogle Scholar
  47. 47.
    Chami M, Robilliard D (2002) Inversion of oceanic constituents in case I and II waters with genetic programming algorithms. Applied Optics 41(30):6260-6275, URL http://ao.osa.org/ViewMedia.cfm?id=70258&seq=0 Google Scholar
  48. 48.
    Channon A 2006 Unbounded evolutionary dynamics in a system of agents that actively process and transform their environment. Genetic Programming and Evolvable Machines 7(3):253-281, DOI doi:10.1007/s10710- 006- 9009- 3Google Scholar
  49. 49.
    Chao DL, Forrest S 2003 Information immune systems. Genetic Programming and Evolvable Machines 4(4):311-331, DOI doi:10.1023/A:1026139027539Google Scholar
  50. 50.
    Cheang SM, Leung KS, Lee KH (2006) Genetic parallel programming: Design and implementation. Evolutionary Computation 14(2):129-156, DOI doi:10. 1162/evco.2006.14.2.129Google Scholar
  51. 51.
    Chen SH(ed)(2002) Genetic Algorithms and Genetic Programming in Computational Finance. Kluwer Academic Publishers, Dordrecht, URL http://www.springer.com/west/home/business?SGWID=4-40517-22-3%3195998-detailsPage=ppmmedia|toc Google Scholar
  52. 52.
    Chen SH, Liao CC (2005) Agent-based computational modeling of the stock price-volume relation. Information Sciences 170(1):75-100, DOI doi:10.1016/j.ins.2003.03.026, URL http://www.sciencedirect.com/science/article/B6V0C-4B3JHTS-6/2/9e023835b1c70f176d1903dd3a8b638e MathSciNetGoogle Scholar
  53. 53.
    Chen SH, Wang HS, Zhang BT (1999) Forecasting high-frequency financial time series with evolutionary neural trees: The case of heng-sheng stock index. In: Arabnia HR (ed) Proceedings of the International Confer-ence on Artificial Intelligence, IC-AI ’99, CSREA Press, Las Vegas, Nevada, USA, vol 2, pp 437-443, URL http://bi.snu.ac.kr/Publications/Conferences/International/ICAI99.ps Google Scholar
  54. 54.
    Chen SH, Duffy J, Yeh CH 2002 Equilibrium selection via adaptation: Using genetic programming to model learning in a coordination game. The Electronic Journal of Evolutionary Modeling and Economic DynamicsGoogle Scholar
  55. 55.
    Chitty DM (2007) A data parallel approach to genetic programming using programmable graphics hardware. In: Thierens D, Beyer HG, Bongard J, Branke J, Clark JA, Cliff D, Congdon CB, Deb K, Doerr B, Kovacs T, Kumar S, Miller JF, Moore J, Neumann F, Pelikan M, Poli R, Sastry K, Stanley KO, Stutzle T, Watson RA, Wegener I (eds) GECCO ’07: Proceed-ings of the 9th annual conference on Genetic and evolutionary computation, ACM Press, London, vol 2, pp 1566-1573, URL http://www.cs.bham.ac.uk/˜wbl/biblio/gecco2007/docs/p1566.pdf Google Scholar
  56. 56.
    Chong FS, Langdon WB (1999) Java based distributed genetic programming on the internet. In: Banzhaf W, Daida J, Eiben AE, Garzon MH, Honavar V, Jakiela M, Smith RE (eds) Proceedings of the Genetic and Evolution-ary Computation Conference, Morgan Kaufmann, Orlando, Florida, USA, vol 2, p 1229, URL http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/p.chong/DGPposter.pdf, full text in technical report CSRP-99-7Google Scholar
  57. 57.
    Ciesielski V, Li X (2004) Analysis of genetic programming runs. In: Mckay RI, Cho SB (eds) Proceedings of The Second Asian-Pacific Workshop on Genetic Programming, Cairns, Australia, URL http://goanna.cs.rmit.edu.au/˜xiali/pub/ai04.vc.pdf Google Scholar
  58. 58.
    Cilibrasi R, Vitanyi PMB (2005) Clustering by compression. IEEE Transactions on Information Theory 51(4):1523-1545, URL http://homepages.cwi.nl/˜paulv/papers/cluster.pdf MathSciNetGoogle Scholar
  59. 59.
    Cilibrasi R, Vitanyi P, de Wolf R (2004) Algorithmic clustering of music based on string compression. Computer Music Journal 28(4):49-67, URL http://homepages.cwi.nl/˜paulv/papers/music.pdf Google Scholar
  60. 60.
    Collins RJ (1992) Studies in artificial evolution. PhD thesis, UCLA, Artificial Life Laboratory, Department of Computer Science, University of California, Los Angeles, LA CA 90024, USAGoogle Scholar
  61. 61.
    Corno F, Sanchez E, Squillero G 2005 Evolving assembly programs: how games help microprocessor validation. Evolutionary Computation, IEEE Transactions on 9(6):695-706Google Scholar
  62. 62.
    Costelloe D, Ryan C (2007) Towards models of user preferences in interac-tive musical evolution. In: Thierens D, Beyer HG, Bongard J, Branke J, Clark JA, Cliff D, Congdon CB, Deb K, Doerr B, Kovacs T, Kumar S, Miller JF, Moore J, Neumann F, Pelikan M, Poli R, Sastry K, Stanley KO, Stutzle T, Watson RA, Wegener I (eds) GECCO ’07: Proceedings of the 9th annual conference on Genetic and evolutionary computation, ACM Press, London, vol 2, pp 2254-2254, URL http://www.cs.bham.ac.uk/˜wbl/biblio/gecco2007/docs/p2254.pdf Google Scholar
  63. 63.
    Cranmer K, Bowman RS (2005) PhysicsGP: A genetic programming approach to event selection. Computer Physics Communications 167(3):165-176, DOI doi:10.1016/j.cpc.2004.12.006Google Scholar
  64. 64.
    Crawford-Marks R, Spector L (2002) Size control via size fair genetic operators in the PushGP genetic programming system. In: Langdon WB, Cantú-Paz E, Mathias K, Roy R, Davis D, Poli R, Balakrishnan K, Honavar V, Rudolph G, Wegener J, Bull L, Potter MA, Schultz AC, Miller JF, Burke E, Jonoska N (eds) GECCO 2002: Proceedings of the Genetic and Evolutionary Computa-tion Conference, Morgan Kaufmann Publishers, New York, pp 733-739, URL http://alum.hampshire.edu/˜rpc01/gp234.pdf Google Scholar
  65. 65.
    Crepeau RL (1995) Genetic evolution of machine language software. In: Rosca JP (ed) Proceedings of the Workshop on Genetic Programming: From Theory to Real-World Applications, Tahoe City, California, USA, pp 121-134, URL http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/GEMS_Article.pdf Google Scholar
  66. 66.
    Curry R, Lichodzijewski P, Heywood MI (2007) Scaling genetic program-ming to large datasets using hierarchical dynamic subset selection. IEEE Transactions on Systems, Man, and Cybernetics: Part B - Cybernetics 37 (4):1065-1073, DOI doi:10.1109/TSMCB.2007.896406, URL http://www.cs.dal.ca/˜mheywood/X-files/GradPubs.html#curry Google Scholar
  67. 67.
    Daida JM, Hommes JD, Bersano-Begey TF, Ross SJ, Vesecky JF (1996) Algorithm discovery using the genetic programming paradigm: Extracting low-contrast curvilinear features from SAR images of arctic ice. In: Angeline PJ, Kinnear, Jr KE (eds) Advances in Genetic Programming 2, MIT Press, Cambridge, MA, USA, chap 21, pp 417-442, URL http://sitemaker.umich.edu/daida/files/GP2_cha21.pdf Google Scholar
  68. 68.
    Dassau E, Grosman B, Lewin DR (2006) Modeling and temperature con-trol of rapid thermal processing. Computers and Chemical Engineering 30 (4):686-697, DOI doi:10.1016/j.compchemeng.2005.11.007, URL http://tx.technion.ac.il/˜dlewin/publications/rtp_paper_v9.pdf Google Scholar
  69. 69.
    Davis TE, Principe JC 1993 A Markov chain framework for the simple genetic algorithm. Evolutionary Computation 1(3):269-288Google Scholar
  70. 70.
    Day JP, Kell DB, Griffith GW (2002) Differentiation of phytophthora infes-tans sporangia from other airborne biological particles by flow cytometry. Applied and Environmental Microbiology 68(1):37-45, DOI doi:10.1128/AEM. 68.1.37- 45.2002, URL http://intl-aem.asm.org/cgi/reprint/68/1/37.pdf Google Scholar
  71. 71.
    de Sousa JS, de CT Gomes L, Bezerra GB, de Castro LN, Von Zuben FJ (2004) An immune-evolutionary algorithm for multiple rearrangements of gene expression data. Genetic Programming and Evolvable Machines 5(2):157-179, DOI doi:10.1023/B:GENP.0000023686.59617.57Google Scholar
  72. 72.
    De Stefano C, Cioppa AD, Marcelli A (2002) Character preclassification based on genetic programming. Pattern Recognition Letters 23(12):1439-1448, DOI doi:10.1016/S0167- 8655(02)00104- 6, URL http://www.sciencedirect.com/science/article/B6V15-45J91MV-4/2/3e5c2ac0c51428d0f7ea9fc0142f6790 zbMATHGoogle Scholar
  73. 73.
    Deb K (2001) Multi-objective optimization using evolutionary algorithms. WileyGoogle Scholar
  74. 74.
    Dempster MAH, Jones CM (2000) A real-time adaptive trading system using genetic programming. Quantitative Finance 1:397-413, URL http://mahd-pc.jbs.cam.ac.uk/archive/PAPERS/2000/geneticprogramming.pdf Google Scholar
  75. 75.
    Dempster MAH, Payne TW, Romahi Y, Thompson GWP (2001) Com-putational learning techniques for intraday FX trading using popular technical indicators. IEEE Transactions on Neural Networks 12 (4):744-754, DOI doi:10.1109/72.935088, URL http://mahd-pc.jbs.cam.ac.uk/archive/PAPERS/2000/ieeetrading.pdf Google Scholar
  76. 76.
    Deschaine L (2006) Using information fusion, machine learning, and global optimisation to increase the accuracy of finding and understanding items interest in the subsurface. GeoDrilling International (122):30-32, URL http://www.mining-journal.com/gdimagazine/pdf/GDI0605scr.pdf Google Scholar
  77. 77.
    Deschaine LM, Patel JJ, Guthrie RD, Grimski JT, Ades MJ (2001) Using linear genetic programming to develop a C/C++ simulation model of a waste incinerator. In: Ades M (ed) Advanced Technology Simulation Conference, Seattle, URL http://www.aimlearning.com/Environmental.Engineering.pdf
  78. 78.
    Deschaine LM, Hoover RA, Skibinski JN, Patel JJ, Francone F, Nordin P, Ades MJ (2002) Using machine learning to compliment and extend the accuracy of UXO discrimination beyond the best reported results of the jefferson proving ground technology demonstration. In: 2002 Advanced Technology Simulation Conference, San Diego, CA, USA, URL http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/deschaine/ASTC2002 UXO Finder Invention Paper.pdf
  79. 79.
    D’haeseleer P (1994) Context preserving crossover in genetic programming. In: Proceedings of the 1994 IEEE World Congress on Computational Intelligence, IEEE Press, Orlando, Florida, USA, vol 1, pp 256-261, URL http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/ftp.io.com/papers/WCCI94_CPC.ps.Z
  80. 80.
    D’haeseleer P, Bluming J (1994) Effects of locality in individual and pop-ulation evolution. In: Kinnear, Jr KE (ed) Advances in Genetic Program-ming, MIT Press, chap 8, pp 177-198, URL http://cognet.mit.edu/library/books/view?isbn=0262111888
  81. 81.
    Dignum S, Poli R (2007) Generalisation of the limiting distribution of program sizes in tree-based genetic programming and analysis of its effects on bloat. In: Thierens D, Beyer HG, Bongard J, Branke J, Clark JA, Cliff D, Congdon CB, Deb K, Doerr B, Kovacs T, Kumar S, Miller JF, Moore J, Neumann F, Pelikan M, Poli R, Sastry K, Stanley KO, Stutzle T, Watson RA, Wegener I (eds) GECCO ’07: Proceedings of the 9th annual conference on Genetic and evolutionary computation, ACM Press, London, vol 2, pp 1588-1595, URL http://www.cs.bham.ac.uk/˜wbl/biblio/gecco2007/docs/p1588.pdf Google Scholar
  82. 82.
    Dolinsky JU, Jenkinson ID, Colquhoun GJ (2007) Application of genetic programming to the calibration of industrial robots. Computers in Industry 58 (3):255-264, DOI doi:10.1016/j.compind.2006.06.003Google Scholar
  83. 83.
    Domingos RP, Schirru R, Martinez AS (2005) Soft computing systems applied to PWR’s xenon. Progress in Nuclear Energy 46(3-4):297-308, DOI doi:10. 1016/j.pnucene.2005.03.011Google Scholar
  84. 84.
    Dracopoulos DC (1997) Evolutionary Learning Algorithms for Neural Adap-tive Control. Perspectives in Neural Computing, Springer Verlag, P.O. Box 31 13 40, D-10643 Berlin, Germany, URL http://www.springer.de/catalog/html-files/deutsch/comp/3540761616.html Google Scholar
  85. 85.
    Droste S, Jansen T, Rudolph G, Schwefel HP, Tinnefeld K, Wegener I (2003) Theory of evolutionary algorithms and genetic programming. In: Schwefel HP, Wegener I, Weinert K (eds) Advances in Computational Intelligence: Theory and Practice, Natural Computing Series, Springer, chap 5, pp 107-144Google Scholar
  86. 86.
    Ebner M, Reinhardt M, Albert J (2005) Evolution of vertex and pixel shaders. In: Keijzer M, Tettamanzi A, Collet P, van Hemert JI, Tomassini M (eds) Proceedings of the 8th European Conference on Genetic Programming, Springer, Lausanne, Switzerland, Lecture Notes in Computer Science, vol 3447, pp 261-270, DOI doi:10.1007/b107383, URL http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=3447&spage=261 Google Scholar
  87. 87.
    Eiben AE, Smith JE (2003) Introduction to Evolutionary Computing. Springer, URL http://www.cs.vu.nl/˜gusz/ecbook/ecbook.html
  88. 88.
    Eklund SE (2002) A massively parallel GP engine in VLSI. In: Fogel DB, El-Sharkawi MA, Yao X, Greenwood G, Iba H, Marrow P, Shackleton M (eds) Proceedings of the 2002 Congress on Evolutionary Computation CEC2002, IEEE Press, pp 629-633Google Scholar
  89. 89.
    Ellis DI, Broadhurst D, Kell DB, Rowland JJ, Goodacre R (2002) Rapid and quantitative detection of the microbial spoilage of meat by fourier trans-form infrared spectroscopy and machine learning. Applied and Environmental Microbiology 68 (6):2822-2828, DOI doi:10.1128/AEM.68.6.2822?2828.2002, URL http://dbkgroup.org/Papers/app%20env_microbiol_68_(2822).pdf Google Scholar
  90. 90.
    Ellis DI, Broadhurst D, Goodacre R (2004) Rapid and quantitative detection of the microbial spoilage of beef by fourier transform infrared spectroscopy and machine learning. Analytica Chimica Acta 514(2):193-201, DOI doi:10.1016/j.aca.2004.03.060, URL http://dbkgroup.org/davefiles/ACAbeef04.pdf Google Scholar
  91. 91.
    Eriksson R, Olsson B (2004) Adapting genetic regulatory models by genetic programming. Biosystems 76 (1-3):217-227, DOI doi:10.1016/j. biosystems.2004.05.014, URL http://www.sciencedirect.com/science/article/B6T2K-4D09KY2-7/2/1abfe196bb4afc60afc3311cadb75d66 Google Scholar
  92. 92.
    Esparcia-Alcazar AI, Sharman KC (1996) Genetic programming techniques that evolve recurrent neural networks architectures for signal processing. In: IEEE Workshop on Neural Networks for Signal Processing, Seiko, Kyoto, JapanGoogle Scholar
  93. 93.
    Evans C, Fleming PJ, Hill DC, Norton JP, Pratt I, Rees D, Rodriguez-Vazquez K (2001) Application of system identification techniques to aircraft gas turbine engines. Control Engineering Practice 9 (2):135-148, URL http://www.sciencedirect.com/science/article/B6V2H-4280YP2-3/1/24d44180070f91dea854032d98f9187a Google Scholar
  94. 94.
    Federman F, Sparkman G, Watt S 1999 Representation of music in a learn-ing classifier system utilizing bach chorales. In: Banzhaf W, Daida J, Eiben AE, Garzon MH, Honavar V, Jakiela M, Smith RE (eds) Proceedings of the Genetic and Evolutionary Computation Conference, Morgan Kaufmann, Orlando, Florida, USA, vol 1, p 785Google Scholar
  95. 95.
    Felton MJ(2000) Survival of the fittest in drug design. Modern Drug Discovery 3(9):49-50, URL http://pubs.acs.org/subscribe/journals/mdd/v03/i09/html/felton.html Google Scholar
  96. 96.
    Fernandez F, Sanchez JM, Tomassini M, Gomez JA (1999) A parallel genetic programming tool based on PVM. In: Dongarra J, Luque E, Margalef T (eds) Recent Advances in Parallel Virtual Machine and Message Passing Interface, Proceedings of the 6th European PVM/MPI Users’ Group Meeting, Springer-Verlag, Barcelona, Spain, Lecture Notes in Computer Science, vol 1697, pp 241-248Google Scholar
  97. 97.
    Fernandez F, Tomassini M, Vanneschi L (2003) An empirical study of multipopulation genetic programming. Genetic Programming and Evolvable Machines 4(1):21-51, DOI doi:10.1023/A:1021873026259zbMATHGoogle Scholar
  98. 98.
    Folino G, Pizzuti C, Spezzano G 2003 A scalable cellular implementa-tion of parallel genetic programming. IEEE Transactions on Evolutionary Computation 7(1):37-53Google Scholar
  99. 99.
    Foster JA 2001 Review: Discipulus: A commercial genetic programming sys-tem. Genetic Programming and Evolvable Machines 2(2):201-203, DOI doi: 10.1023/A:1011516717456MathSciNetGoogle Scholar
  100. 100.
    Francone FD, Deschaine LM (2004) Getting it right at the very start - building project models where data is expensive by combining human expertise, machine learning and information theory. In: 2004 Business and Industry Sym-posium, Washington, DC, URL http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/deschaine/ASTC_2004_Getting_It_Right_from_the_Very_Start.pdf
  101. 101.
    Francone FD, Conrads M, Banzhaf W, Nordin P (1999) Homologous crossover in genetic programming. In: Banzhaf W, Daida J, Eiben AE, Garzon MH, Honavar V, Jakiela M, Smith RE (eds) Proceedings of the Genetic and Evolu-tionary Computation Conference, Morgan Kaufmann, Orlando, Florida, USA, vol 2, pp 1021-1026, URL http://www.cs.bham.ac.uk/˜wbl/biblio/gecco1999/GP-463.pdf Google Scholar
  102. 102.
    Francone FD, Deschaine LM, Warren JJ (2007) Discrimination of munitions and explosives of concern at F.E. warren AFB using linear genetic program-ming. In: Thierens D, Beyer HG, Bongard J, Branke J, Clark JA, Cliff D, Con-gdon CB, Deb K, Doerr B, Kovacs T, Kumar S, Miller JF, Moore J, Neumann F, Pelikan M, Poli R, Sastry K, Stanley KO, Stutzle T, Watson RA, Wegener I (eds) GECCO ’07: Proceedings of the 9th annual conference on Genetic and evolutionary computation, ACM Press, London, vol 2, pp 1999-2006, URL http://www.cs.bham.ac.uk/˜wbl/biblio/gecco2007/docs/p1999.pdf Google Scholar
  103. 103.
    Fukunaga A (2002) Automated discovery of composite SAT variable selection heuristics. In: Proceedings of the National Conference on Artificial Intelligence (AAAI), pp 641-648Google Scholar
  104. 104.
    Fukunaga AS (2004) Evolving local search heuristics for SAT using genetic programming. In: Deb K, Poli R, Banzhaf W, Beyer HG, Burke E, Darwen P, Dasgupta D, Floreano D, Foster J, Harman M, Holland O, Lanzi PL, Spector L, Tettamanzi A, Thierens D, Tyrrell A (eds) Genetic and Evolutionary Com-putation - GECCO-2004, Part II, Springer-Verlag, Seattle, WA, USA, Lecture Notes in Computer Science, vol 3103, pp 483-494, DOI doi:10.1007/b98645, URL http://alexf04.maclisp.org/gecco2004.pdf Google Scholar
  105. 105.
    Funes P, Sklar E, Juille H, Pollack J (1998a) Animal-animat coevolution: Using the animal population as fitness function. In: Pfeifer R, Blumberg B, Meyer JA, Wilson SW (eds) From Animals to Animats 5: Proceedings of the Fifth International Conference on Simulation of Adaptive Behavior., MIT Press, Zurich, Switzerland, pp 525-533, URL http://www.demo.cs.brandeis.edu/papers/tronsab98.html Google Scholar
  106. 106.
    Funes P, Sklar E, Juille H, Pollack J (1998b) Animal-animat coevolution: Using the animal population as fitness function. In: Pfeifer R, Blumberg B, Meyer JA, Wilson SW (eds) From Animals to Animats 5: Proceedings of the Fifth International Conference on Simulation of Adaptive Behavior, MIT Press, Zurich, Switzerland, pp 525-533, URL http://www.demo.cs.brandeis.edu/papers/tronsab98.pdf Google Scholar
  107. 107.
    Gagne C, Parizeau M(2006) Genetic engineering of hierarchical fuzzy regional representations for handwritten character recognition. International Journal on Document Analysis and Recognition 8(4):223-231, DOI doi: 10.1007/s10032- 005- 0005- 6, URL http://vision.gel.ulaval.ca/fr/publications/Id_607/PublDetails.php Google Scholar
  108. 108.
    Gagné C, Parizeau M (2007) Co-evolution of nearest neighbor classifiers. International Journal of Pattern Recognition and Artificial Intelligence 21 (5):921-946, DOI doi:10.1142/S0218001407005752, URL http://vision.gel.ulaval.ca/en/publications/Id_692/PublDetails.php Google Scholar
  109. 109.
    Garcia-Almanza AL, Tsang EPK (2006) Forecasting stock prices using genetic programming and chance discovery. In: 12th International Confer-ence On Computing In Economics And Finance, p number 489, URL repec.org/sce2006/up.13879.1141401469.pdfGoogle Scholar
  110. 110.
    Gathercole C, Ross P (1994) Dynamic training subset selection for supervised learning in genetic programming. In: Davidor Y, Schwefel HP, Männer R (eds) Parallel Problem Solving from Nature III, Springer-Verlag, Jerusalem, LNCS, vol 866, pp 312-321, URL http://citeseer.ist.psu.edu/gathercole94dynamic.html Google Scholar
  111. 111.
    Gathercole C, Ross P (1997) Tackling the boolean even N parity problem with genetic programming and limited-error fitness. In: Koza JR, Deb K, Dorigo M, Fogel DB, Garzon M, Iba H, Riolo RL (eds) Genetic Programming 1997: Proceedings of the Second Annual Conference, Morgan Kaufmann, Stanford University, CA, USA, pp 119-127, URL http://citeseer.ist.psu.edu/79389.html Google Scholar
  112. 112.
    Gelly S, Teytaud O, Bredeche N, Schoenauer M (2006) Universal consistency and bloat in GP. Revue d’Intelligence Artificielle 20(6):805-827, URL http://hal.inria.fr/docs/00/11/28/40/PDF/riabloat.pdf, issue on New Methods in Machine Learning. Theory and ApplicationsGoogle Scholar
  113. 113.
    Gilbert RJ, Goodacre R, Woodward AM, Kell DB (1997) Genetic programming: A novel method for the quantitative analysis of pyrolysis mass spectral data. ANALYTICAL CHEMISTRY 69(21):4381-4389, DOI doi: 10.1021/ac970460j, URL http://pubs.acs.org/journals/ancham/article.cgi/ancham/1997/69/i21/pdf/ac970460j.pdf Google Scholar
  114. 114.
    Globus A, Lawton J, Wipke T (1998) Automatic molecular design using evolutionary techniques. In: Globus A, Srivastava D (eds) The Sixth Fore-sight Conference on Molecular Nanotechnology, Westin Hotel in Santa Clara, CA, USA, URL http://www.foresight.org/Conferences/MNT6/Papers/Globus/index.html Google Scholar
  115. 115.
    Goldberg DE (1989) Genetic Algorithms in Search Optimization and Machine Learning. Addison-WesleyGoogle Scholar
  116. 116.
    Goldberg DE, Kargupta H, Horn J, Cantu-Paz E (1995) Critical deme size for serial and parallel genetic algorithms. Tech. rep., Illinois Genetic Algo-rithms Laboratory, Department of General Engineering, University of Illinois at Urbana-Champaign, Il 61801, USA, illiGAL Report no 95002Google Scholar
  117. 117.
    Goodacre R (2003) Explanatory analysis of spectroscopic data using machine learning of simple, interpretable rules. Vibrational Spectroscopy 32 (1):33-45, DOI doi:10.1016/S0924- 2031(03)00045- 6, URL http://www.biospec.net/learning/Metab06/Goodacre-FTIRmaps.pdf, a collection of Papers Presented at Shedding New Light on Disease: Optical Diagnostics for the New Millennium (SPEC 2002) Reims, France 23-27 June 2002Google Scholar
  118. 118.
    Goodacre R, Gilbert RJ (1999) The detection of caffeine in a variety of beverages using curie-point pyrolysis mass spectrometry and genetic programming. The Analyst 124:1069-1074Google Scholar
  119. 119.
    Goodacre R, Shann B, Gilbert RJ, Timmins EM, McGovern AC, Alsberg BK, Kell DB, Logan NA (2000) The detection of the dipicolinic acid biomarker in bacillus spores using curie-point pyrolysis mass spectrometry and fourier-transform infrared spectroscopy. Analytical Chemistry 72(1):119-127, DOI doi:10.1021/ac990661i, URL http://pubs.acs.org/cgi-bin/article.cgi/ancham/2000/72/i01/html/ac990661i.html Google Scholar
  120. 120.
    Goodacre R, Vaidyanathan S, Dunn WB, Harrigan GG, Kell DB (2004) Metabolomics by numbers: acquiring and understanding global metabolite data. Trends in Biotechnology 22(5):245-252, DOI doi:10.1016/j.tibtech.2004. 03.007, URL http://dbkgroup.org/Papers/trends%20in%20biotechnology_22(24%5).pdf Google Scholar
  121. 121.
    Gruau F (1994a) Genetic micro programming of neural networks. In: Kinnear, Jr KE (ed) Advances in Genetic Programming, MIT Press, chap 24, pp 495-518, URL http://cognet.mit.edu/library/books/view?isbn=0262111888
  122. 122.
    Gruau F (1994b) Neural network synthesis using cellular encoding and the genetic algorithm. PhD thesis, Laboratoire de l’Informatique du Parallilisme, Ecole Normale Supirieure de Lyon, France, URL ftp://ftp.ens-lyon.fr/pub/LIP/Rapports/PhD/PhD1994/PhD1994-01-E.ps.ZGoogle Scholar
  123. 123.
    Gruau F (1996) On using syntactic constraints with genetic programming. In: Angeline PJ, Kinnear, Jr KE (eds) Advances in Genetic Programming 2, MIT Press, Cambridge, MA, USA, chap 19, pp 377-394Google Scholar
  124. 124.
    Gruau F, Whitley D 1993 Adding learning to the cellular development process: a comparative study. Evolutionary Computation 1(3):213-233Google Scholar
  125. 125.
    Gustafson S, Burke EK (2006) The speciating island model: An alternative parallel evolutionary algorithm. Journal of Parallel and Distributed Computing 66(8):1025-1036, DOI doi:10.1016/j.jpdc.2006.04.017, parallel Bioinspired AlgorithmszbMATHGoogle Scholar
  126. 126.
    Gustafson S, Burke EK, Krasnogor N 2005 On improving genetic programming for symbolic regression. In: Corne D, Michalewicz Z, Dorigo M, Eiben G, Fogel D, Fonseca C, Greenwood G, Chen TK, Raidl G, Zalzala A, Lucas S, Paechter B, Willies J, Guervos JJM, Eberbach E, McKay B, Channon A, Tiwari A, Volkert LG, Ashlock D, Schoenauer M (eds) Proceedings of the 2005 IEEE Congress on Evolutionary Computation, IEEE Press, Edinburgh, UK, vol 1, pp 912-919Google Scholar
  127. 127.
    Hampo RJ, Marko KA (1992) Application of genetic programming to control of vehicle systems. In: Proceedings of the Intelligent Vehicles ’92 Symposium, june 29 July 1, 1992, Detroit, Mi, USAGoogle Scholar
  128. 128.
    Handley S 1993 Automatic learning of a detector for alpha-helices in protein sequences via genetic programming. In: Forrest S (ed) Proceedings of the 5th International Conference on Genetic Algorithms, ICGA-93, Morgan Kaufmann, University of Illinois at Urbana-Champaign, pp 271-278Google Scholar
  129. 129.
    Handley S (1994) On the use of a directed acyclic graph to represent a popula-tion of computer programs. In: Proceedings of the 1994 IEEE World Congress on Computational Intelligence, IEEE Press, Orlando, Florida, USA, vol 1, pp 154-159, DOI doi:10.1109/ICEC.1994.350024Google Scholar
  130. 130.
    Harding S, Banzhaf W (2007) Fast genetic programming on GPUs. In: Ebner M, O’Neill M, Ekárt A, Vanneschi L, Esparcia-Alcázar AI (eds) Proceedings of the 10th European Conference on Genetic Programming, Springer, Valencia, Spain, Lecture Notes in Computer Science, vol 4445, pp 90-101, DOI doi: 10.1007/978- 3- 540- 71605- 1 9Google Scholar
  131. 131.
    Harrigan GG, LaPlante RH, Cosma GN, Cockerell G, Goodacre R, Maddox JF, Luyendyk JP, Ganey PE, Roth RA (2004) Application of high-throughput fourier-transform infrared spectroscopy in toxicology studies: contribution to a study on the development of an animal model for idiosyncratic toxicity. Toxicology Letters 146(3):197-205, DOI doi:10.1016/j.toxlet.2003.09.011Google Scholar
  132. 132.
    Harris C, Buxton B (1996) GP-COM: A distributed, component-based genetic programming system in C++. In: Koza JR, Goldberg DE, Fogel DB, Riolo RL (eds) Genetic Programming 1996: Proceedings of the First Annual Con-ference, MIT Press, Stanford University, CA, USA, p 425, URL http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp96com.ps.gz Google Scholar
  133. 133.
    Harvey B, Foster J, Frincke D (1999) Towards byte code genetic programming. In: Banzhaf W, Daida J, Eiben AE, Garzon MH, Honavar V, Jakiela M, Smith RE (eds) Proceedings of the Genetic and Evolutionary Computation Conference, Morgan Kaufmann, Orlando, Florida, USA, vol 2, p 1234, URL http://citeseer.ist.psu.edu/468509.html Google Scholar
  134. 134.
    Hasan S, Daugelat S, Rao PSS, Schreiber M (2006) Prioritizing genomic drug targets in pathogens: Application to mycobacterium tuberculosis. PLoS Computational Biology 2(6):e61, DOI doi:10.1371/journal.pcbi.0020061Google Scholar
  135. 135.
    Hauptman A, Sipper M (2005) GP-endchess: Using genetic programming to evolve chess endgame players. In: Keijzer M, Tettamanzi A, Collet P, van Hemert JI, Tomassini M (eds) Proceedings of the 8th European Con-ference on Genetic Programming, Springer, Lausanne, Switzerland, Lecture Notes in Computer Science, vol 3447, pp 120-131, URL http://www.cs.bgu.ac.il/˜sipper/papabs/eurogpchess-final.pdf Google Scholar
  136. 136.
    Hauptman A, Sipper M (2007) Evolution of an efficient search algorithm for the mate-in-N problem in chess. In: Ebner M, O’Neill M, Ekárt A, Vanneschi L, Esparcia-Alcázar AI (eds) Proceedings of the 10th European Conference on Genetic Programming, Springer, Valencia, Spain, Lecture Notes in Computer Science, vol 4445, pp 78-89, DOI doi:10.1007/978- 3- 540- 71605- 1 8Google Scholar
  137. 137.
    Haynes T, Wainwright R, Sen S, Schoenefeld D (1995) Strongly typed genetic programming in evolving cooperation strategies. In: Eshelman L (ed) Genetic Algorithms: Proceedings of the Sixth International Conference (ICGA95), Morgan Kaufmann, Pittsburgh, PA, USA, pp 271-278, URL http://www.mcs.utulsa.edu/˜rogerw/papers/Haynes-icga95.pdf Google Scholar
  138. 138.
    Haynes TD, Schoenefeld DA, Wainwright RL (1996) Type inheritance in strongly typed genetic programming. In: Angeline PJ, Kinnear, Jr KE (eds) Advances in Genetic Programming 2, MIT Press, Cambridge, MA, USA, chap 18, pp 359-376, URL http://www.mcs.utulsa.edu/˜rogerw/papers/Haynes-hier.pdf Google Scholar
  139. 139.
    Heidema AG, Boer JMA, Nagelkerke N, Mariman ECM, van der A DL, Feskens EJM (2006) The challenge for genetic epidemiologists: how to ana-lyze large numbers of SNPs in relation to complex diseases. BMC Genet-ics 7(23), DOI doi:10.1186/1471- 2156- 7- 23, URL http://www.biomedcentral.com/content/pdf/1471-2156-7-23.pdf
  140. 140.
    Hillis WD 1992 Co-evolving parasites improve simulated evolution as an optimization procedure. In: Langton CG, Taylor CE, Farmer JD, Rasmussen S (eds) Artificial Life II, Santa Fe Institute Studies in the Sciences of Complexity, vol X, Addison-Wesley, Santa Fe Institute, New Mexico, USA, pp 313-324Google Scholar
  141. 141.
    Hinchliffe MP, Willis MJ (2003) Dynamic systems modeling using genetic programming. Computers & Chemical Engineering 27(12):1841-1854, URL http://www.sciencedirect.com/science/article/B6TFT-49MDYGW-2/2/742bcc7f22240c7a0381027aa5ff7e73 Google Scholar
  142. 142.
    Ho SY, Hsieh CH, Chen HM, Huang HL (2006) Interpretable gene expression classifier with an accurate and compact fuzzy rule base for microarray data analysis. Biosystems 85(3):165-176, DOI doi:10.1016/j.biosystems.2006.01.002Google Scholar
  143. 143.
    Hoai NX, McKay RI, Abbass HA (2003) Tree adjoining grammars, language bias, and genetic programming. In: Ryan C, Soule T, Keijzer M, Tsang E, Poli R, Costa E (eds) Genetic Programming, Proceedings of EuroGP’2003, Springer-Verlag, Essex, LNCS, vol 2610, pp 335-344, URL http://www.cs.adfa.edu.au/˜abbass/publications/hardcopies/TAG3P-EuroGp-03.pdf Google Scholar
  144. 144.
    Hoai NX, McKay RIB, Essam D (2006) Representation and structural dif-ficulty in genetic programming. IEEE Transactions on Evolutionary Com-putation 10(2):157-166, DOI doi:10.1109/TEVC.2006.871252, URL http://sc.snu.ac.kr/courses/2006/fall/pg/aai/GP/nguyen/Structdiff.pdf Google Scholar
  145. 145.
    Holland J 1975 Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, USAGoogle Scholar
  146. 146.
    Holland JH (1992) Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence. MIT Press, first Published by University of Michigan Press 1975Google Scholar
  147. 147.
    Hong JH, Cho SB (2006) The classification of cancer based on DNA microarray data that uses diverse ensemble genetic programming. Artificial Intelligence In Medicine 36(1):43-58, DOI doi:10.1016/j.artmed.2005.06.002Google Scholar
  148. 148.
    Howard D, Roberts SC 2004 Incident detection on highways. In: O’Reilly UM, Yu T, Riolo RL, Worzel B (eds) Genetic Programming Theory and Practice II, Springer, Ann Arbor, chap 16, pp 263-282Google Scholar
  149. 149.
    Howard D, Roberts SC, Brankin R (1999) Target detection in imagery by genetic programming. Advances in Engineering Software 30(5):303-311, URL http://www.sciencedirect.com/science/article/B6V1P-3W1XV4H-1/1/6e7aee809f33757d0326c62a21824411 Google Scholar
  150. 150.
    Howard D, Roberts SC, Ryan C (2006) Pragmatic genetic programming strategy for the problem of vehicle detection in airborne reconnaissance. Pattern Recognition Letters 27(11):1275-1288, DOI doi:10.1016/j.patrec.2005.07.025, evolutionary Computer Vision and Image UnderstandingGoogle Scholar
  151. 151.
    Iba H (1996) Genetic Programming. Tokyo Denki University PressGoogle Scholar
  152. 152.
    Iba H, de Garis H, Sato T (1994) Genetic programming using a minimum description length principle. In: Kinnear, Jr KE (ed) Advances in Genetic Pro-gramming, MIT Press, chap 12, pp 265-284, URL http://citeseer.ist.psu.edu/327857.html
  153. 153.
    Inagaki Y (2002) On synchronized evolution of the network of automata. IEEE Transactions on Evolutionary Computation 6(2):147-158, URL http://ieeexplore.ieee.org/iel5/4235/21497/00996014.pdf?tp=&arnumber=996014&isnumber=21497&arSt=147&ared=158&arAuthor=Inagaki%2C+Y.%3B MathSciNetGoogle Scholar
  154. 154.
    Jacob C 1997 Principia Evolvica - Simulierte Evolution mit Mathematica. dpunkt.verlag, Heidelberg, GermanyGoogle Scholar
  155. 155.
    Jacob C (2000) The art of genetic programming. IEEE Intelligent Systems 15(3):83-84, URL http://ieeexplore.ieee.org/iel5/5254/18363/00846288.pdf Google Scholar
  156. 156.
    Jacob C(2001) Illustrating Evolutionary Computation with Mathematica. Morgan Kaufmann, URL http://www.mkp.com/bookscatalog/catalog.asp?ISBN=1-55860-637-8
  157. 157.
    Jeong KS, Kim DK, Whigham P, Joo GJ (2003) Modeling microcystis aeruginosa bloom dynamics in the nakdong river by means of evolutionary computa-tion and statistical approach. Ecological Modeling 161(1-2):67-78, DOI doi:10. 1016/S0304- 3800(02)00280- 6, URL http://www.business.otago.ac.nz/infosci/SIRC/PeterW/Publications/Jeong_EcolMod_V161_Is_1_2_pg67_78.pdf Google Scholar
  158. 158.
    Jin N, Tsang E (2006) Co-adaptive strategies for sequential bargaining problems with discount factors and outside options. In: Proceedings of the 2006 IEEE Congress on Evolutionary Computation, IEEE Press, Vancouver, pp 7913-7920Google Scholar
  159. 159.
    Johnson HE, Gilbert RJ, Winson MK, Goodacre R, Smith AR, Rowland JJ, Hall MA, Kell DB 2000 Explanatory analysis of the metabolome using genetic programming of simple, interpretable rules. Genetic Programming and Evolvable Machines 1(3):243-258, DOI doi:10.1023/A:1010014314078zbMATHGoogle Scholar
  160. 160.
    Jones A, Young D, Taylor J, Kell DB, Rowland JJ 1998 Quantification of microbial productivity via multi-angle light scattering and supervised learning. Biotechnology and Bioengineering 59(2):131-143Google Scholar
  161. 161.
    Jordaan E, Kordon A, Chiang L, Smits G (2004) Robust inferential sensors based on ensemble of predictors generated by genetic programming. In: Yao X, Burke E, Lozano JA, Smith J, Merelo-Guervós JJ, Bullinaria JA, Rowe J, Kabán PTA, Schwefel HP (eds) Parallel Problem Solving from Nature - PPSN VIII, Springer-Verlag, Birmingham, UK, LNCS, vol 3242, pp 522-531, DOI doi:10.1007/b100601, URL http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3242&spage=522 Google Scholar
  162. 162.
    Juille H, Pollack JB (1996) Massively parallel genetic programming. In: Angeline PJ, Kinnear, Jr KE (eds) Advances in Genetic Programming 2, MIT Press, Cambridge, MA, USA, chap 17, pp 339-358, URL http://www.demo.cs.brandeis.edu/papers/gp2.pdf Google Scholar
  163. 163.
    Kaboudan M 1999 A measure of time series predictability using genetic programming applied to stock returns. Journal of Forecasting 18:345-357Google Scholar
  164. 164.
    Kaboudan M 2005 Extended daily exchange rates forecasts using wavelet temporal resolutions. New Mathematics and Natural Computing 1:79-107zbMATHGoogle Scholar
  165. 165.
    Kaboudan MA 2000 Genetic programming prediction of stock prices. Computational Economics 6(3):207-236Google Scholar
  166. 166.
    Keijzer M 1996 Efficiently representing populations in genetic programming. In: Angeline PJ, Kinnear, Jr KE (eds) Advances in Genetic Programming 2, MIT Press, Cambridge, MA, USA, chap 13, pp 259-278Google Scholar
  167. 167.
    Keijzer M (2004) Scaled symbolic regression. Genetic Programming and Evolvable Machines 5(3):259-269, DOI doi:10.1023/B:GENP.0000030195.77571.f9Google Scholar
  168. 168.
    Kell D (2002a) Defence against the flood. Bioinformatics World pp 16-18, URL http://dbkgroup.org/Papers/biwpp16-18as_publ.pdf
  169. 169.
    Kell DB (2002b) Genotype-phenotype mapping: genes as computer programs. Trends in Genetics 18(11):555-559, DOI doi:10.1016/S0168- 9525(02)02765- 8, URL http://dbkgroup.org/Papers/trendsgenet_18_(555).pdf Google Scholar
  170. 170.
    Kell DB (2002c) Metabolomics and machine learning: Explanatory analysis of complex metabolome data using genetic programming to produce simple, robust rules. Molecular Biology Reports 29(1-2):237-241, DOI doi:10.1023/A: 1020342216314, URL http://dbkgroup.org/Papers/btk2002dbk.pdf Google Scholar
  171. 171.
    Kell DB, Darby RM, Draper J 2001 Genomic computing. explanatory analy-sis of plant expression profiling data using machine learning. Plant Physiology 126 (3):943-951Google Scholar
  172. 172.
    Keller RE, Poli R (2007a) Cost-benefit investigation of a genetic-programming hyperheuristic. In: Proceedings of Evolution ArtificielleGoogle Scholar
  173. 173.
    Keller RE, Poli R (2007b) Linear genetic programming of metaheuristics. In: Thierens D, Beyer HG, Bongard J, Branke J, Clark JA, Cliff D, Congdon CB, Deb K, Doerr B, Kovacs T, Kumar S, Miller JF, Moore J, Neumann F, Pelikan M, Poli R, Sastry K, Stanley KO, Stutzle T, Watson RA, Wegener I (eds) GECCO ’07: Proceedings of the 9th annual conference on Genetic and evolutionary computation, ACM Press, London, vol 2, pp 1753-1753, URL http://www.cs.bham.ac.uk/˜wbl/biblio/gecco2007/docs/p1753.pdf Google Scholar
  174. 174.
    Keller RE, Poli R (2007c) Linear genetic programming of parsimonious meta-heuristics. In: Proceedings of IEEE Congress on Evolutionary Computation (CEC)Google Scholar
  175. 175.
    KHosraviani B (2003) Organization design optimization using genetic programming. In: Koza JR (ed) Genetic Algorithms and Genetic Programming at Stanford 2003, Stanford Bookstore, Stanford, California, 94305-3079 USA, pp 109-117, URL http://www.genetic-programming.org/sp2003/KHosraviani.pdf Google Scholar
  176. 176.
    KHosraviani B, Levitt RE, Koza JR (2004) Organization design optimization using genetic programming. In: Keijzer M (ed) Late Breaking Papers at the 2004 Genetic and Evolutionary Computation Conference, Seattle, Washington, USA, URL http://www.cs.bham.ac.uk/˜wbl/biblio/gecco2004/LBP056.pdf
  177. 177.
    Kibria RH, Li Y (2006) Optimizing the initialization of dynamic decision heuristics in DPLL SAT solvers using genetic programming. In: Collet P, Tomassini M, Ebner M, Gustafson S, Ekárt A (eds) Proceedings of the 9th European Conference on Genetic Programming, Springer, Budapest, Hungary, Lecture Notes in Computer Science, vol 3905, pp 331-340, URL http://link.springer.de/link/service/series/0558/papers/3905/39050331.pdf Google Scholar
  178. 178.
    Kinnear, Jr KE (1993) Evolving a sort: Lessons in genetic programming. In: Proceedings of the 1993 International Conference on Neural Networks, IEEE Press, San Francisco, USA, vol 2, pp 881-888, DOI doi:10.1109/ICNN. 1993.298674, URL http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/ftp.io.com/papers/kinnear.icnn93.ps.Z
  179. 179.
    Kinnear, Jr KE(ed)(1994a) Advances in Genetic Programming. MIT Press, Cambridge, MA, URL http://mitpress.mit.edu/book-home.tcl?isbn=0262111888 Google Scholar
  180. 180.
    Kinnear, Jr KE (1994b) Fitness landscapes and difficulty in genetic programming. In: Proceedings of the 1994 IEEE World Conference on Computational Intelligence, IEEE Press, Orlando, Florida, USA, vol 1, pp 142-147, DOI doi: 10.1109/ICEC.1994.350026, URL http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/ftp.io.com/papers/kinnear.wcci.ps.Z
  181. 181.
    Kinnear, Jr KE (1994c) A perspective on the work in this book. In: Kinnear, Jr KE (ed) Advances in Genetic Programming, MIT Press, chap 1, pp 3-19, URL http://cognet.mit.edu/library/books/view?isbn=0262111888
  182. 182.
    Klassen TJ, Heywood MI (2002) Towards the on-line recognition of arabic characters. In: Proceedings of the 2002 International Joint Conference on Neural Networks IJCNN’02, IEEE Press, Hilton Hawaiian Village Hotel, Hon-olulu, Hawaii, pp 1900-1905, URL http://users.cs.dal.ca/˜mheywood/X-files/Publications/IEEEarabic.pdf
  183. 183.
    Klein J, Spector L (2007) Unwitting distributed genetic programming via asynchronous javascript and XML. In: Thierens D, Beyer HG, Bongard J, Branke J, Clark JA, Cliff D, Congdon CB, Deb K, Doerr B, Kovacs T, Kumar S, Miller JF, Moore J, Neumann F, Pelikan M, Poli R, Sastry K, Stanley KO, Stutzle T, Watson RA, Wegener I (eds) GECCO ’07: Proceedings of the 9th annual conference on Genetic and evolutionary computation, ACM Press, London, vol 2, pp 1628-1635, URL http://www.cs.bham.ac.uk/˜wbl/biblio/gecco2007/docs/p1628.pdf Google Scholar
  184. 184.
    Kordon A (2006) Evolutionary computation at dow chemical. SIGEVOlution 1 (3):4-9, URL http://www.sigevolution.org/2006/03/issue.pdf Google Scholar
  185. 185.
    Kordon A, Castillo F, Smits G, Kotanchek M 2005 Application issues of genetic programming in industry. In: Yu T, Riolo RL, Worzel B (eds) Genetic Programming Theory and Practice III, Genetic Programming, vol 9, Springer, Ann Arbor, chap 16, pp 241-258Google Scholar
  186. 186.
    Kovacic M, Balic J (2003) Evolutionary programming of a CNC cutting machine. International journal for advanced manufacturing technology 22 (1-2):118-124, DOI doi:10.1007/s00170- 002- 1450- 8, URL http://www.springerlink.com/openurl.asp?genre=article&eissn=1433-3015&volume=22&issue=1&spage=118 Google Scholar
  187. 187.
    Koza JR (1990) A genetic approach to econometric modeling. In: Sixth World Congress of the Econometric Society, Barcelona, Spain, URL http://www.genetic-programming.com/jkpdf/wces1990.pdf
  188. 188.
    Koza JR 1992 Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA, USAzbMATHGoogle Scholar
  189. 189.
    Koza JR 1994a Genetic Programming II: Automatic Discovery of Reusable Programs. MIT Press, Cambridge MassachusettszbMATHGoogle Scholar
  190. 190.
    Koza JR 1994b Genetic Programming II Videotape: The next generation. MIT Press, 55 Hayward Street, Cambridge, MA, USAGoogle Scholar
  191. 191.
    Koza JR, Andre D (1996) Classifying protein segments as transmembrane domains using architecture-altering operations in genetic programming. In: Angeline PJ, Kinnear, Jr KE (eds) Advances in Genetic Programming 2, MIT Press, Cambridge, MA, USA, chap 8, pp 155-176, URL http://www.genetic-programming.com/jkpdf/aigp2aatmjk1996.pdf Google Scholar
  192. 192.
    Koza JR, Poli R (2005) Genetic programming. In: Burke EK, Kendall G (eds) Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques, Springer, chap 5, URL http://www.springer.com/sgw/cda/frontpage/0,11855,4-10045-22-67933962-0,00.html
  193. 193.
    Koza JR, Andre D, Bennett III FH, Keane MA (1996a) Use of automatically defined functions and architecture-altering operations in automated circuit syn-thesis using genetic programming. In: Koza JR, Goldberg DE, Fogel DB, Riolo RL (eds) Genetic Programming 1996: Proceedings of the First Annual Con-ference, MIT Press, Stanford University, CA, USA, pp 132-149, URL http://www.genetic-programming.com/jkpdf/gp1996adfaa.pdf Google Scholar
  194. 194.
    Koza JR, Bennett III FH, Andre D, Keane MA (1996b) Automated WYWIWYG design of both the topology and component values of electrical circuits using genetic programming. In: Koza JR, Goldberg DE, Fogel DB, Riolo RL (eds) Genetic Programming 1996: Proceedings of the First Annual Confer-ence, MIT Press, Stanford University, CA, USA, pp 123-131, URL http://www.genetic-programming.com/jkpdf/gp1996nielsen.pdf Google Scholar
  195. 195.
    Koza JR, Andre D, Bennett III FH, Keane M (1999a) Genetic Programming 3: Darwinian Invention and Problem Solving. Morgan Kaufman, URL http://www.genetic-programming.org/gpbook3toc.html
  196. 196.
    Koza JR, Bennett III FH, Stiffelman O (1999b) Genetic programming as a Darwinian invention machine. In: Poli R, Nordin P, Langdon WB, Fogarty TC (eds) Genetic Programming, Proceedings of EuroGP’99, Springer-Verlag, Goteborg, Sweden, LNCS, vol 1598, pp 93-108, URL http://www.genetic-programming.com/jkpdf/eurogp1999.pdf Google Scholar
  197. 197.
    Koza JR, Keane MA, Streeter MJ, Mydlowec W, Yu J, Lanza G (2003) Genetic Programming IV: Routine Human-Competitive Machine Intelligence. Kluwer Academic Publishers, URL http://www.genetic-programming.org/gpbook4toc.html
  198. 198.
    Koza JR, Al-Sakran SH, Jones LW (2005) Automated re-invention of six patented optical lens systems using genetic programming. In: Beyer HG, O’Reilly UM, Arnold DV, Banzhaf W, Blum C, Bonabeau EW, Cantu-Paz E, Dasgupta D, Deb K, Foster JA, de Jong ED, Lipson H, Llora X, Man-coridis S, Pelikan M, Raidl GR, Soule T, Tyrrell AM, Watson JP, Zitzler E (eds) GECCO 2005: Proceedings of the 2005 conference on Genetic and evolu-tionary computation, ACM Press, Washington DC, USA, vol 2, pp 1953-1960, URL http://www.cs.bham.ac.uk/˜wbl/biblio/gecco2005/docs/p1953.pdf Google Scholar
  199. 199.
    Krasnogor N (2004) Self generating metaheuristics in bioinformatics: The proteins structure comparison case. Genetic Programming and Evolvable Machines 5 (2):181-201, DOI doi:10.1023/B:GENP.0000023687.41210.d7Google Scholar
  200. 200.
    Krawiec K (2004) Evolutionary Feature Programming: Cooperative learning for knowledge discovery and computer vision. 385, Wydawnictwo Politechniki Poznanskiej, Poznan University of Technology, Poznan, Poland , URL http://idss.cs.put.poznan.pl/˜krawiec/pubs/hab/krawiec_hab.pdf Google Scholar
  201. 201.
    Langdon WB 1996 A bibliography for genetic programming. In: Angeline PJ, Kinnear, Jr KE (eds) Advances in Genetic Programming 2, MIT Press, Cambridge, MA, USA, chap B, pp 507-532, URL http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/WBL.aigp2.appx.ps.gz Google Scholar
  202. 202.
    Langdon WB 1998a The evolution of size in variable length representations. In: 1998 IEEE International Conference on Evolutionary Computa-tion, IEEE Press, Anchorage, Alaska, USA, pp 633-638, DOI doi:10.1109/ICEC.1998.700102, URL http://www.cs.bham.ac.uk/˜wbl/ftp/papers/WBL.wcci98_bloat.pdf Google Scholar
  203. 203.
    Langdon WB (1998b) Genetic Programming and Data Structures: Genetic Programming + Data Structures = Automatic Programming!, Genetic Program-ming, vol 1. Kluwer, Boston, URL http://www.cs.ucl.ac.uk/staff/W.Langdon/gpdata Google Scholar
  204. 204.
    Langdon WB(1999a) Scaling of program tree fitness spaces. Evolutionary Computation7(4):399-428, URL http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/WBL.fitnessspaces.pdf Google Scholar
  205. 205.
    Langdon WB 1999b Size fair and homologous tree genetic programming crossovers. In: Banzhaf W, Daida J, Eiben AE, Garzon MH, Honavar V, Jakiela M, Smith RE (eds) Proceedings of the Genetic and Evolutionary Computation Conference, Morgan Kaufmann, Orlando, Florida, USA, vol 2, pp 1092-1097, URL http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/WBL.gecco99.fairxo.ps.gz Google Scholar
  206. 206.
    Langdon WB 2000 Size fair and homologous tree genetic programming crossovers. Genetic Programming and Evolvable Machines 1(1/2):95-119, DOI doi:10.1023/A:1010024515191, URL http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/WBL_fairxo.pdf zbMATHGoogle Scholar
  207. 207.
    Langdon WB 2002a Convergence rates for the distribution of program outputs. In: Langdon WB, Cantú-Paz E, Mathias K, Roy R, Davis D, Poli R, Balakrishnan K, Honavar V, Rudolph G, Wegener J, Bull L, Potter MA, Schultz AC, Miller JF, Burke E, Jonoska N (eds) GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference, Morgan Kauf-mann Publishers, New York, pp 812-819, URL http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/wbl_gecco2002.pdf Google Scholar
  208. 208.
    Langdon WB 2002b How many good programs are there? How long are they? In: De Jong KA, Poli R, Rowe JE (eds) Foundations of Genetic Algorithms VII, Morgan Kaufmann, Torremolinos, Spain, pp 183-202, URL http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/wbl_foga2002.pdf, published 2003Google Scholar
  209. 209.
    Langdon WB 2003a Convergence of program fitness landscapes. In: Cantú-Paz E, Foster JA, Deb K, Davis D, Roy R, O’Reilly UM, Beyer HG, Standish R, Kendall G, Wilson S, Harman M, Wegener J, Dasgupta D, Pot-ter MA, Schultz AC, Dowsland K, Jonoska N, Miller J (eds) Genetic and Evolutionary Computation - GECCO-2003, Springer-Verlag, Chicago, LNCS, vol 2724, pp1702-1714, URL http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/wbl_gecco2003.pdf Google Scholar
  210. 210.
    Langdon WB (2003b) The distribution of reversible functions is Normal. In: Riolo RL, Worzel B (eds) Genetic Programming Theory and Practise, Kluwer, chap 11, pp 173-188, URL http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/wbl_reversible.pdf
  211. 211.
    Langdon WB 2004 Global distributed evolution of L-systems fractals. In: Keijzer M, O’Reilly UM, Lucas SM, Costa E, Soule T (eds) Genetic Pro-gramming, Proceedings of EuroGP’2004, Springer-Verlag, Coimbra, Portugal, LNCS, vol 3003, pp 349-358, URL http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/egp2004_pfeiffer.pdf Google Scholar
  212. 212.
    Langdon WB 2005a The distribution of amorphous computer outputs. In: Stepney S, Emmott S (eds) The Grand Challenge in Non-Classical Com-putation: International Workshop, York, UK, URL http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/grand_2005.pdf Google Scholar
  213. 213.
    Langdon WB 2005b Pfeiffer - A distributed open-ended evolutionary system. In: Edmonds B, Gilbert N, Gustafson S, Hales D, Krasnogor N (eds) AISB’05: Proceedings of the Joint Symposium on Socially Inspired Computing (METAS 2005), University of Hertfordshire, Hatfield, UK, pp 7-13, URL http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/wbl_metas2005.pdf, sSAISB 2005 ConventionGoogle Scholar
  214. 214.
    Langdon WB 2006 Mapping non-conventional extensions of genetic programming. In: Calude CS, Dinneen MJ, Paun G, Rozenberg G, Stepney S (eds) Unconventional Computing 2006, Springer-Verlag, York, LNCS, vol 4135, pp 166-180, DOI doi:10.1007/11839132 14, URL http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/wbl_uc2002.pdf Google Scholar
  215. 215.
    Langdon WB, Banzhaf W 2005 Repeated sequences in linear genetic programming genomes. Complex Systems 15(4):285-306, URL http://www.cs.ucl.ac.uk/staff/rW.Langdon/ftp/papers/wbl_repeat_linear.pdf zbMATHMathSciNetGoogle Scholar
  216. 216.
    Langdon WB, Banzhaf W (2007) A SIMD interpreter for genetic programming on GPU graphics cards. In preparationGoogle Scholar
  217. 217.
    Langdon WB, Buxton BF 2004 Genetic programming for mining DNA chip data from cancer patients. Genetic Programming and Evolvable Machines 5(3):251-257, DOI doi:10.1023/B:GENP.0000030196.55525.f7, URL http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/wbldnachip.pdf Google Scholar
  218. 218.
    Langdon WB, Harrison AP (2008) GP on SPMD parallel graphics hardware for mega bioinformatics data mining, To appearGoogle Scholar
  219. 219.
    Langdon WB, Nordin P 2001 Evolving hand-eye coordination for a humanoid robot with machine code genetic programming. In: Miller JF, Tomassini M, Lanzi PL, Ryan C, Tettamanzi AGB, Langdon WB (eds) Genetic Program-ming, Proceedings of EuroGP’2001, Springer-Verlag, Lake Como, Italy, LNCS, vol 2038, pp 313-324, URL http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/wbl_handeye.ps.gz Google Scholar
  220. 220.
    Langdon WB, Poli R 2008 Mapping non-conventional extensions of geneticprogramming. Natural Computing 7:21-43. Invited contribution to special issue on Unconventional computingzbMATHMathSciNetGoogle Scholar
  221. 221.
    Langdon WB, Poli R 1997 Fitness causes bloat. In: Chawdhry PK, Roy R, Pant RK (eds) Soft Computing in Engineering Design and Manufacturing, Springer-Verlag London, pp 13-22, URL http://www.rcs.bham.ac.uk/˜wbl/ftp/papers/WBL.bloat_wsc2.ps.gz Google Scholar
  222. 222.
    Langdon WB, Poli R (2002) Foundations of Genetic Programming. SpringerVerlag, URL http://www.cs.ucl.ac.uk/staff/W.Langdon/FOGP/
  223. 223.
    Langdon WB, Poli R 2006a The halting probability in von Neumann architectures. In: Collet P, Tomassini M, Ebner M, Gustafson S, Ekárt A (eds) Pro-ceedings of the 9th European Conference on Genetic Programming, Springer, Budapest, Hungary, Lecture Notes in Computer Science, vol 3905, pp 225-237, URL http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/wblegp2006.pdf Google Scholar
  224. 224.
    Langdon WB, Poli R (2006b) On turing complete T7 and MISC F-4 program fitness landscapes. In: Arnold DV, Jansen T, Vose MD, Rowe JE (eds) Theory of Evolutionary Algorithms, Internationales Begegnungs-und Forschungszentrum fuer Informatik (IBFI), Schloss Dagstuhl, Germany, Dagstuhl, Germany, no. 06061 in Dagstuhl Seminar Proceedings, URL http://drops.dagstuhl.de/opus/volltexte/2006/595, <http://drops.dagstuhl.de/opus/volltexte/2006/595> [date of citation: 2006-01-01] [date of citation: 2006-01-01]
  225. 225.
    Langdon WB, Soule T, Poli R, Foster JA 1999 The evolution of size and shape. In: Spector L, Langdon WB, O’Reilly UM, Angeline PJ (eds) Advances in Genetic Programming 3, MIT Press, Cambridge, MA, USA, chap 8, pp 163-190, URL http://www.cs.bham.ac.uk/˜wbl/aigp3/ch08.pdf Google Scholar
  226. 226.
    Leung KS, Lee KH, Cheang SM (2002) Genetic parallel programming - evolving linear machine codes on a multiple-ALU processor. In: Yaacob S, Nagarajan R, Chekima A (eds) Proceedings of International Conference on Artificial Intelligence in Engineering and Technology - ICAIET 2002, Universiti Malaysia Sabah, pp 207-213Google Scholar
  227. 227.
    Lew TL, Spencer AB, Scarpa F, Worden K, Rutherford A, Hemez F 2006 Identification of response surface models using genetic programming. Mechanical Systems and Signal Processing 20(8):1819-1831, DOI doi:10.1016/j.ymssp. 2005.12.003Google Scholar
  228. 228.
    Lewin DR, Lachman-Shalem S, Grosman B 2006 The role of process system engineering (PSE) in integrated circuit (IC) manufacturing. Control Engineer-ing Practice 15(7):793-802, DOI doi:10.1016/j.conengprac.2006.04.003, special Issue on Award Winning Applications, 2005 IFAC World CongressGoogle Scholar
  229. 229.
    Li L, Jiang W, Li X, Moser KL, Guo Z, Du L, Wang Q, Topol EJ, Wang Q, Rao S 2005 A robust hybrid between genetic algorithm and support vector machine for extracting an optimal feature gene subset. Genomics 85(1):16-23, DOI doi:10.1016/j.ygeno.2004.09.007Google Scholar
  230. 230.
    Linden R, Bhaya A 2007 Evolving fuzzy rules to model gene expression. Biosystems 88(1-2):76-91, DOI doi:10.1016/j.biosystems.2006.04.006Google Scholar
  231. 231.
    Lipson H 2004 How to draw a straight line using a GP: Benchmarking evolutionary design against 19th century kinematic synthesis. In: Keijzer M (ed) Late Breaking Papers at the 2004 Genetic and Evolutionary Compu-tation Conference, Seattle, Washington, USA, URL http://www.cs.bham.ac.uk/˜wbl/biblio/gecco2004/LBP063.pdf Google Scholar
  232. 232.
    Liu W, Schmidt B (2006) Mapping of hierarchical parallel genetic algorithms for protein folding onto computational grids. IEICE Transactions on Information and Systems E89-D(2):589-596, DOI doi:10.1093/ietisy/e89-d.2. 589Google Scholar
  233. 233.
    Lohn J, Hornby G, Linden D (2004) Evolutionary antenna design for a NASA spacecraft. In: O’Reilly UM, Yu T, Riolo RL, Worzel B(eds) Genetic Programming Theory and Practice II, Springer, Ann Arbor, chap 18, pp 301-315Google Scholar
  234. 234.
    Lohn JD, Hornby GS, Linden DS 2005 Rapid re-evolution of an X-band antenna for NASA’s space technology 5 mission. In: Yu T, Riolo RL, Worzel B (eds) Genetic Programming Theory and Practice III, Genetic Programming, vol 9, Springer, Ann Arbor, chap 5, pp 65-78Google Scholar
  235. 235.
    Louchet J 2001 Using an individual evolution strategy for stereovision. Genetic Programming and Evolvable Machines 2(2):101-109, DOI doi:10.1023/A:1011544128842zbMATHGoogle Scholar
  236. 236.
    Louchet J, Guyon M, Lesot MJ, Boumaza A 2002 Dynamic flies: a new pattern recognition tool applied to stereo sequence processing. Pattern Recognition Letters 23(1-3):335-345, DOI doi:10.1016/S0167- 8655(01)00129- 5zbMATHGoogle Scholar
  237. 237.
    Loviscach J, Meyer-Spradow J (2003) Genetic programming of vertex shaders. In: Chover M, Hagen H, Tost D (eds) Proceedings of EuroMedia 2003, pp 29-31Google Scholar
  238. 238.
    Luke S (1998) Evolving soccerbots: A retrospective. In: Proceedings of the 12th Annual Conference of the Japanese Society for Artificial Intelligence, URL http://www.cs.gmu.edu/˜sean/papers/robocupShort.pdf
  239. 239.
    Luke S 2000 Two fast tree-creation algorithms for genetic programming. IEEE Transactions on Evolutionary Computation 4(3):274-283, URL http://ieeexplore.ieee.org/iel5/4235/18897/00873237.pdf Google Scholar
  240. 240.
    Lukschandl E, Borgvall H, Nohle L, Nordahl M, Nordin P (2000) Distributed java bytecode genetic programming. In: Poli R, Banzhaf W, Langdon WB, Miller JF, Nordin P, Fogarty TC (eds) Genetic Programming, Proceedings of EuroGP’2000, Springer-Verlag, Edinburgh, LNCS, vol 1802, pp 316-325, URL http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1802&spage=316 Google Scholar
  241. 241.
    Machado P, Romero J (eds) (2008) The Art of Artificial Evolution. SpringerGoogle Scholar
  242. 242.
    Marenbach P 1998 Using prior knowledge and obtaining process insight in data based modeling of bioprocesses. System Analysis Modeling Simulation 31:39-59zbMATHGoogle Scholar
  243. 243.
    Markose S, Tsang E, Er H, Salhi A (2001) Evolutionary arbitrage for FTSE-100 index options and futures. In: Proceedings of the 2001 Congress on Evolutionary Computation CEC2001, IEEE Press, COEX, World Trade Cen-ter, 159 Samseong-dong, Gangnam-gu, Seoul, Korea, pp 275-282, DOI doi: 10.1109/CEC.2001.934401, URL http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/TsangCEE2001.pdf
  244. 244.
    Marney JP, Miller D, Fyfe C, Tarbert HFE (2001) Risk adjusted returns to technical trading rules: a genetic programming approach. In: 7th International Conference of Society of Computational Economics, YaleGoogle Scholar
  245. 245.
    Martin MC (2006) Evolving visual sonar: Depth from monocular images. Pattern Recognition Letters 27(11):1174-1180, DOI doi:10.1016/j.patrec. 2005.07.015, URL http://martincmartin.com/papers/EvolvingVisualSonar-PatternRecognitionLetters2006.pdf, evolutionary Computer Vision and Image UnderstandingGoogle Scholar
  246. 246.
    Martin P 2001 A hardware implementation of a genetic programming system using FPGAs and Handel-C. Genetic Programming and Evolvable Machines 2(4):317-343, DOI doi:10.1023/A:1012942304464, URL http://www.naiadhome.com/gpem-d.pdf zbMATHGoogle Scholar
  247. 247.
    Massey P, Clark JA, Stepney S 2005 Evolution of a human-competitive quantum fourier transform algorithm using genetic programming. In: Beyer HG, O’Reilly UM, Arnold DV, Banzhaf W, Blum C, Bonabeau EW, Cantu-Paz E, Dasgupta D, Deb K, Foster JA, de Jong ED, Lipson H, Llora X, Mancoridis S, Pelikan M, Raidl GR, Soule T, Tyrrell AM, Watson JP, Zitzler E (eds) GECCO 2005: Proceedings of the 2005 conference on Genetic and evolutionary compu-tation, ACM Press, Washington DC, USA, vol 2, pp 1657-1663, URL http://www.cs.bham.ac.uk/˜wbl/biblio/gecco2005/docs/p1657.pdf Google Scholar
  248. 248.
    Maxwell III SR (1994) Experiments with a coroutine model for genetic programming. In: Proceedings of the1994IEEE World Congress on Computational Intelligence, IEEE Press, Orlando, Florida, USA, vol 1, pp 413-417a, URL http://ieeexplore.ieee.org/iel2/1125/8059/00349915.pdf? isNumber=8059
  249. 249.
    McCormack J (2006) New challenges for evolutionary music and art. SIGEvolution 1(1):5-11, URL http://www.sigevolution.org/2006/01/issue.pdf MathSciNetGoogle Scholar
  250. 250.
    McGovern AC, Broadhurst D, Taylor J, Kaderbhai N, Winson MK, Small DA, Rowland JJ, Kell DB, Goodacre R 2002 Monitoring of complex industrial bioprocesses for metabolite concentrations using modern spectroscopies and machine learning: Application to gibberellic acid production. Biotechnology and Bioengineering 78(5):527-538, DOI doi:10.1002/bit.10226, URL http://dbkgroup.org/Papers/biotechnol_bioeng_78_(527).pdf Google Scholar
  251. 251.
    McKay B, Willis M, Searson D, Montague G (2000) Nonlinear continuum regression: an evolutionary approach. Transactions of the Institute of Mea-surement and Control 22(2):125-140, doi:10.1177/014233120002200202, URL http://www.ingentaconnect.com/content/arn/tm/2000/00000022/00000002/art00007 Google Scholar
  252. 252.
    McPhee NF, Miller JD 1995 Accurate replication in genetic programming. In: Eshelman L (ed) Genetic Algorithms: Proceedings of the Sixth International Conference (ICGA95), Morgan Kaufmann, Pittsburgh, PA, USA, pp 303-309, URL http://www.mrs.umn.edu/˜mcphee/Research/Accurate_replication.ps Google Scholar
  253. 253.
    McPhee NF, Hopper NJ, Reierson ML 1998 Sutherland: An extensible object-oriented software framework for evolutionary computation. In: Koza JR, Banzhaf W, Chellapilla K, Deb K, Dorigo M, Fogel DB, Garzon MH, Goldberg DE, Iba H, Riolo R (eds) Genetic Programming 1998: Proceedings of the Third Annual Conference, Morgan Kaufmann, University of Wisconsin, Madison, Wisconsin, USA, p 241, URL http://www.mrs.umn.edu/˜mcphee/Research/Sutherland/rsutherland_gp98_announcement.ps.gz Google Scholar
  254. 254.
    Mercure PK, Smits GF, Kordon A (2001) Empirical emulators for first principle models. In: AIChE Fall Annual Meeting, Reno Hilton, URL http://www.aiche.org/conferences/techprogram/paperdetail.asp?PaperID=2373&DSN=annual01
  255. 255.
    Meyer-Spradow J, Loviscach J (2003) Evolutionary design of BRDFs. In: Chover M, Hagen H, Tost D (eds) Eurographics 2003 Short Paper Pro-ceedings, pp 301-306, URL http://viscg.uni-muenster.de/publications/2003/ML03/evolutionary web.pdf
  256. 256.
    Miller JF 1999 An empirical study of the efficiency of learning boolean functions using a cartesian genetic programming approach. In: Banzhaf W, Daida J, Eiben AE, Garzon MH, Honavar V, Jakiela M, Smith RE (eds) Pro-ceedings of the Genetic and Evolutionary Computation Conference, Morgan Kaufmann, Orlando, Florida, USA, vol 2, pp 1135-1142, URL http://citeseer.ist.psu.edu/153431.html Google Scholar
  257. 257.
    Miller JF, Smith SL 2006 Redundancy and computational efficiency in cartesian genetic programming. IEEE Transactions on Evolutionary Computation 10 (2):167-174, DOI doi:10.1109/TEVC.2006.871253Google Scholar
  258. 258.
    Mitavskiy B, Rowe J 2006 Some results about the markov chains associated to GPs and to general EAs. Theoretical Computer Science 361(1):72-110, DOI doi:10.1016/j.tcs.2006.04.006zbMATHMathSciNetGoogle Scholar
  259. 259.
    Montana DJ(1995) Strongly typed genetic programming. Evolutionary Computation 3(2):199-230, URL http://vishnu.bbn.com/papers/stgp.pdf Google Scholar
  260. 260.
    Moore GE (1965) Cramming more components onto integrated circuits. Electronics 38(8):114-117Google Scholar
  261. 261.
    Moore JH, Parker JS, Olsen NJ, Aune TM (2002) Symbolic discriminant analysis of microarray data in automimmune disease. Genetic Epidemiology 23:57-69Google Scholar
  262. 262.
    Motsinger AA, Lee SL, Mellick G, Ritchie MD (2006) GPNN: Power studies and applications of a neural network method for detecting gene-gene interactions in studies of human disease. BMC bioinformatics[electronic resource] 7(1):39-39, DOI doi:10.1186/1471- 2105- 7- 39, URL http://www.biomedcentral.com/1471-2105/7/39 Google Scholar
  263. 263.
    Neely CJ 2003 Risk-adjusted, ex ante, optimal technical trading rules in equity markets. International Review of Economics and Finance 12(1):69-87, DOI doi:10.1016/S1059- 0560(02)00129- 6, URL http://research.stlouisfed.org/wp/1999/1999-015.pdf Google Scholar
  264. 264.
    Neely CJ, Weller PA 1999 Technical trading rules in the european monetary system. Journal of International Money and Finance 18(3):429-458, DOI doi:10.1016/S0261- 5606(99)85005- 0, URL http://research.stlouisfed.org/wp/1997/97-015.pdf Google Scholar
  265. 265.
    Neely CJ, Weller PA (2001a) Predicting exchange rate volatility: Genetic programming vs. GARCH and risk metrics. Working Paper 2001-009B, Economic, Research, Federal Reserve Bank of St. Louis, 411 Locust Street, St. Louis, MO 63102-0442, USA, URL http://research.stlouisfed.org/wp/2001/2001-009.pdf
  266. 266.
    Neely CJ, Weller PA 2001b Technical analysis and central bank inter-vention. Journal of International Money and Finance 20(7):949-970, DOI doi:10.1016/S0261- 5606(01)00033-X, URL http://research.stlouisfed.org/wp/1997/97-002.pdf Google Scholar
  267. 267.
    Neely CJ, Weller PA, Dittmar R (1997) Is technical analysis in the foreign exchange market profitable? A genetic programming approach. The Journal of Financial and Quantitative Analysis 32(4):405-426, URL http://links.jstor.org/sici?sici=0022-1090%28199712%2932%3A4%3C405%3AITAITF%3_E2.0.CO%3B2-T Google Scholar
  268. 268.
    Neely CJ, Weller PA, Ulrich JM (2006) The adaptive markets hypothesis: evidence from the foreign exchange market. Working Paper 2006-046B, Fed-eral Reserve Bank of St. Louis, Research Division, P.O. Box 442, St. Louis, MO 63166, USA, URL http://research.stlouisfed.org/wp/2006/2006-046.pdf, revised March 2007
  269. 269.
    Nikolaev N, Iba H (2006) Adaptive Learning of Polynomial Networks Genetic Programming, Backpropagation and Bayesian Methods. No. 4 in Genetic and Evolutionary Computation, Springer, juneGoogle Scholar
  270. 270.
    Nikolaev NY, Iba H (2002) Genetic programming of polynomial models for financial forecasting. In: Chen SH(ed) Genetic Algorithms and Genetic Programming in Computational Finance, Kluwer Academic Press, chap 5, pp 103-123Google Scholar
  271. 271.
    Nix AE, Vose MD 1992 Modeling genetic algorithms with Markov chains. Annals of Mathematics and Artificial Intelligence 5:79-88zbMATHMathSciNetGoogle Scholar
  272. 272.
    Nordin P (1994) A compiling genetic programming system that directly manipulates the machine code. In: Kinnear, Jr KE (ed) Advances in Genetic Programming, MIT Press, chap 14, pp 311-331, URL http://cognet.mit.edu/library/books/view?isbn=0262111888
  273. 273.
    Nordin P (1997) Evolutionary program induction of binary machine code and its applications. PhD thesis, der Universitat Dortmund am Fachereich InformatikGoogle Scholar
  274. 274.
    Nordin P, Johanna W (2003) Humanoider: Sjavlarande robotar och artificiell intelligens. LiberGoogle Scholar
  275. 275.
    Nordin P, Banzhaf W, Francone FD 1999 Efficient evolution of machine code for CISC architectures using instruction blocks and homologous crossover. In: Spector L, Langdon WB, O’Reilly UM, Angeline PJ (eds) Advances in Genetic Programming 3, MIT Press, Cambridge, MA, USA, chap 12, pp 275-299, URL http://www.aimlearning.com/aigp31.pdf Google Scholar
  276. 276.
    Oakley H (1994) Two scientific applications of genetic programming: Stack filters and non-linear equation fitting to chaotic data. In: Kinnear, Jr KE (ed) Advances in Genetic Programming, MIT Press, chap 17, pp 369-389, URL http://cognet.mit.edu/library/books/view?isbn=0262111888
  277. 277.
    Oltean M 2005 Evolving evolutionary algorithms using linear genetic programming. Evolutionary Computation 13(3):387-410, DOI doi:10.1162/1063656054794815, URL http://www.ingentaconnect.com/content/mitpress/evco/2005/00000013/00000003/art00006 Google Scholar
  278. 278.
    Oltean M, Dumitrescu D(2004) Evolving TSP heuristics using multi expression programming. In: Bubak M, van Albada GD, Sloot PMA, Dongarra J(eds) Computational Science- ICCS2004:4th International Conference, Part II, Springer-Verlag, Krakow, Poland, Lecture Notes in Computer Science, vol 3037, pp 670-673, DOI doi:10.1007/b97988, URL http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=3037&spage=670 Google Scholar
  279. 279.
    O’Neill M, Ryan C (2003) Grammatical Evolution: Evolutionary Automatic Programming in a Arbitrary Language, Genetic programming, vol 4. Kluwer Academic Publishers, URL http://www.wkap.nl/prod/b/1-4020-7444-1
  280. 280.
    Openshaw S, Turton I 1994 Building new spatial interaction models using genetic programming. In: Fogarty TC (ed) Evolutionary Computing, Springer-Verlag, Leeds, UK, Lecture Notes in Computer Science, URL http://www.geog.leeds.ac.uk/papers/94-1/94-1.pdf Google Scholar
  281. 281.
    O’Reilly UM (1995) An analysis of genetic programming. PhD thesis, Carleton University, Ottawa-Carleton Institute for Computer Science, Ottawa, Ontario, Canada, URL http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/oreilly/abstract.ps.gz Google Scholar
  282. 282.
    O’Reilly UM, Hemberg M 2007 Integrating generative growth and evolutionary computation for form exploration. Genetic Programming and Evolvable Machines 8(2):163-186, DOI doi:10.1007/s10710- 007- 9025-y, special issue on developmental systemsGoogle Scholar
  283. 283.
    O’Reilly UM, Oppacher F(1994) The troubling aspects of a building block hypothesis for genetic programming. In: Whitley LD, Vose MD (eds) Foundations of Genetic Algorithms 3, Morgan Kaufmann, Estes Park, Colorado, USA, pp 73-88, URL http://citeseer.ist.psu.edu/cache/papers/cs/163/http:zSzzSzwww.ai.mit.eduzSzpeoplezSzunamayzSzpaperszSzfoga.pdf/oreilly92troubling.pdf, published 1995Google Scholar
  284. 284.
    O’Reilly UM, Yu T, Riolo RL, Worzel B (eds) 2004 Genetic Programming Theory and Practice II, Genetic Programming, vol 8, Springer, Ann Arbor, MI, USA, URL http://www.springeronline.com/sgw/cda/frontpage/0,11855,_5-40356-22-34954683-0,00.html Google Scholar
  285. 285.
    Oussaidène M, Chopard B, Pictet OV, Tomassini M 1997 Parallel genetic programming and its application to trading model induction. Parallel Computing 23 (8):1183-1198, URL http://citeseer.ist.psu.edu/cache/papers/cs/166/http:zSzzSzlsl http://www.epfl.chzSzmarcozSzparcomp.pdf/oussaidene97parallel.pdf zbMATHGoogle Scholar
  286. 286.
    Owens JD, David, Govindaraju N, Harris M, Kruger J, Lefohn AE, Purcell TJ 2007 A survey of general-purpose computation on graphics hardware. Computer Graphics Forum 26(1):80-113, DOI doi:10.1111/j.1467- 8659.2007. 01012.xGoogle Scholar
  287. 287.
    Parrott D, Li X, Ciesielski V 2005 Multi-objective techniques in genetic programming for evolving classifiers. In: Corne D, Michalewicz Z, Dorigo M, Eiben G, Fogel D, Fonseca C, Greenwood G, Chen TK, Raidl G, Zalzala A, Lucas S, Paechter B, Willies J, Guervos JJM, Eberbach E, McKay B, Channon A, Tiwari A, Volkert LG, Ashlock D, Schoenauer M (eds) Pro-ceedings of the 2005 IEEE Congress on Evolutionary Computation, IEEE Press, Edinburgh, UK, vol 2, pp 1141-1148, URL http://goanna.cs.rmit.edu.au/˜xiaodong/publications/183.pdf Google Scholar
  288. 288.
    Perkis T (1994) Stack-based genetic programming. In: Proceedings of the 1994 IEEE World Congress on Computational Intelligence, IEEE Press, Orlando, Florida, USA, vol 1, pp 148-153, URL http://citeseer.ist.psu.edu/432690.html
  289. 289.
    Pillay N 2003 Evolving solutions to ASCII graphics programming problems in intelligent programming tutors. In: Akerkar R (ed) International Conference on Applied Artificial Intelligence (ICAAI’2003), TMRF, Fort Panhala, Kolhapur, India, pp 236-243Google Scholar
  290. 290.
    Poli R (1996a) Discovery of symbolic, neuro-symbolic and neural networks with parallel distributed genetic programming. Tech. Rep. CSRP-96-14, University of Birmingham, School of Computer Science, URL ftp://ftp.cs. bham.ac.uk/pub/tech-reports/1996/CSRP-96-14.ps.gz, presented at 3rd Inter-national Conference on Artificial Neural Networks and Genetic Algorithms, ICANNGA’97Google Scholar
  291. 291.
    Poli R (1996b) Genetic programming for image analysis. In: Koza JR, Goldberg DE, Fogel DB, Riolo RL (eds) Genetic Programming 1996: Proceedings of the First Annual Conference, MIT Press, Stanford University, CA, USA, pp 363-368, URL http://cswww.essex.ac.uk/staff/rpoli/papers/Poli-GP1996.pdf Google Scholar
  292. 292.
    Poli R 1999 Parallel distributed genetic programming. In: Corne D, Dorigo M, Glover F (eds) New Ideas in Optimization, Advanced Topics in Computer Science, McGraw-Hill, Maidenhead, Berkshire, England, chap 27, pp 403-431, URL http://citeseer.ist.psu.edu/328504.html Google Scholar
  293. 293.
    Poli R 2000a Exact schema theorem and effective fitness for GP with one-point crossover. In: Whitley D, Goldberg D, Cantu-Paz E, Spector L, Parmee I, Beyer HG (eds) Proceedings of the Genetic and Evolutionary Computation Conference, Morgan Kaufmann, Las Vegas, pp 469-476Google Scholar
  294. 294.
    Poli R 2000b Hyperschema theory for GP with one-point crossover, building blocks, and some new results in GA theory. In: Poli R, Banzhaf W, Langdon WB, Miller JF, Nordin P, Fogarty TC (eds) Genetic Programming, Proceedings of EuroGP’2000, Springer-Verlag, Edinburgh, LNCS, vol 1802, pp 163-180, URL http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1802&spage=163 Google Scholar
  295. 295.
    Poli R 2001 Exact schema theory for genetic programming and variable-length genetic algorithms with one-point crossover. Genetic Programming and Evolvable Machines 2(2):123-163zbMATHMathSciNetGoogle Scholar
  296. 296.
    Poli R 2003 A simple but theoretically-motivated method to control bloat in genetic programming. In: Ryan C, Soule T, Keijzer M, Tsang E, Poli R, Costa E (eds) Genetic Programming, Proceedings of EuroGP’2003, Springer-Verlag, Essex, LNCS, vol 2610, pp 204-217, URL http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2610&spage=204 Google Scholar
  297. 297.
    Poli R 2005 Tournament selection, iterated coupon-collection problem, and backward-chaining evolutionary algorithms. In: Wright AH, Vose MD, De Jong KA, Schmitt LM (eds) Foundations of Genetic Algorithms 8, Springer-Verlag, Aizu-Wakamatsu City, Japan, Lecture Notes in Computer Science, vol 3469, pp 132-155, URL http://www.cs.essex.ac.uk/staff/rpoli/papers/foga2005_Poli.pdf Google Scholar
  298. 298.
    Poli R, Langdon WB 1997 A new schema theory for genetic programming with one-point crossover and point mutation. In: Koza JR, Deb K, Dorigo M, Fogel DB, Garzon M, Iba H, Riolo RL (eds) Genetic Programming 1997: Proceedings of the Second Annual Conference, Morgan Kaufmann, Stanford University, CA, USA, pp 278-285, URL http://citeseer.ist.psu.edu/327495.html Google Scholar
  299. 299.
    Poli R, Langdon WB 1998a On the search properties of different crossover operators in genetic programming. In: Koza JR, Banzhaf W, Chellapilla K, Deb K, Dorigo M, Fogel DB, Garzon MH, Goldberg DE, Iba H, Riolo R (eds) Genetic Programming 1998: Proceedings of the Third Annual Conference, Morgan Kaufmann, University of Wisconsin, Madison, Wisconsin, USA, pp 293-301, URL http://www.cs.essex.ac.uk/staff/poli/papers/Poli-GP1998.pdf Google Scholar
  300. 300.
    Poli R, Langdon WB 1998b Schema theory for genetic programming with one-point crossover and point mutation. Evolutionary Computation 6(3):231-252, URL http://cswww.essex.ac.uk/staff/poli/papers/Poli-ECJ1998.pdf Google Scholar
  301. 301.
    Poli R, Langdon WB (2005a) Running genetic programming backward. In: Riolo RL, Worzel B, Yu T (eds) Genetic Programming Theory and Practice, KluwerGoogle Scholar
  302. 302.
    Poli R, Langdon WB 2005b Running genetic programming backward. In: Yu T, Riolo RL, Worzel B (eds) Genetic Programming Theory and Practice III, Genetic Programming, vol 9, Springer, Ann Arbor, chap 9, pp 125-140, URL http://www.cs.essex.ac.uk/staff/poli/papers/GPTP2005.pdf Google Scholar
  303. 303.
    Poli R, Langdon WB 2006a Backward-chaining evolutionary algorithms. Artificial Intelligence 170(11):953-982, DOI doi:10.1016/j.artint.2006.04.003, URL http://www.cs.essex.ac.uk/staff/poli/papers/aijournal2006.pdf zbMATHMathSciNetGoogle Scholar
  304. 304.
    Poli R, Langdon WB 2006b Efficient markov chain model of machine code program execution and halting. In: Riolo RL, Soule T, Worzel B (eds) Genetic Programming Theory and Practice IV, Genetic and Evolutionary Computa-tion, vol 5, Springer, Ann Arbor, chap 13, URL http://www.cs.essex.ac.uk/staff/poli/papers/GPTP2006.pdf Google Scholar
  305. 305.
    Poli R, McPhee NF 2003a General schema theory for genetic programming with subtree-swapping crossover: Part I. Evolutionary Computation 11(1):53-66, DOI doi:10.1162/106365603321829005, URL http://cswww.essex.ac.uk/staff/rpoli/papers/ecj2003partI.pdf Google Scholar
  306. 306.
    Poli R, McPhee NF 2003b General schema theory for genetic programming with subtree-swapping crossover: Part II. Evolutionary Computation 11 (2):169-206, DOI doi:10.1162/106365603766646825, URL http://cswww.essex.ac.uk/staff/rpoli/papers/ecj2003partII.pdf Google Scholar
  307. 307.
    Poli R, Page J, Langdon WB 1999 Smooth uniform crossover, sub-machine code GP and demes: A recipe for solving high-order boolean parity problems. In: Banzhaf W, Daida J, Eiben AE, Garzon MH, Honavar V, Jakiela M, Smith RE (eds) Proceedings of the Genetic and Evolutionary Computation Conference, Morgan Kaufmann, Orlando, Florida, USA, vol 2, pp 1162-1169, URL http://www.cs.bham.ac.uk/˜wbl/biblio/gecco1999/GP-466.pdf Google Scholar
  308. 308.
    Poli R, Rowe JE, McPhee NF 2001 Markov chain models for GP and variable-length GAs with homologous crossover. In: Spector L, Goodman ED, Wu A, Langdon WB, Voigt HM, Gen M, Sen S, Dorigo M, Pezeshk S, Garzon MH, Burke E (eds) Proceedings of the Genetic and Evolutionary Computation Con-ference (GECCO-2001), Morgan Kaufmann, San Francisco, California, USA, pp 112-119, URL http://www.cs.bham.ac.uk/˜wbl/biblio/gecco2001/d01.pdf Google Scholar
  309. 309.
    Poli R, McPhee NF, Rowe JE 2004 Exact schema theory and markov chain models for genetic programming and variable-length genetic algorithms with homologous crossover. Genetic Programming and Evolvable Machines 5(1):31-70, DOI doi:10.1023/B:GENP.0000017010.41337.a7, URL http://cswww.essex.ac.uk/staff/rpoli/papers/GPEM2004.pdf Google Scholar
  310. 310.
    Poli R, Di Chio C, Langdon WB 2005a Exploring extended particle swarms: a genetic programming approach. In: Beyer HG, O’Reilly UM, Arnold DV, Banzhaf W, Blum C, Bonabeau EW, Cantu-Paz E, Dasgupta D, Deb K, Foster JA, de Jong ED, Lipson H, Llora X, Mancoridis S, Pelikan M, Raidl GR, Soule T, Tyrrell AM, Watson JP, Zitzler E (eds) GECCO 2005: Proceedings of the 2005 conference on Genetic and evolutionary computation, ACM Press, Washington DC, USA, vol 1, pp 169-176, URL http://www.cs.essex.ac.uk/staff/poli/papers/geccopso2005.pdf Google Scholar
  311. 311.
    Poli R, Langdon WB, Holland O 2005b Extending particle swarm optimisation via genetic programming. In: Keijzer M, Tettamanzi A, Collet P, van Hemert JI, Tomassini M (eds) Proceedings of the 8th European Conference on Genetic Programming, Springer, Lausanne, Switzerland, Lecture Notes in Computer Science, vol 3447, pp 291-300, URL http://www.cs.essex.ac.uk/staff/poli/papers/eurogpPSO2005.pdf Google Scholar
  312. 312.
    Poli R, Langdon WB, Dignum S 2007a On the limiting distribution of program sizes in tree-based genetic programming. In: Ebner M, O’Neill M, Ekárt A, Vanneschi L, Esparcia-Alcázar AI (eds) Proceedings of the 10th European Conference on Genetic Programming, Springer, Valencia, Spain, Lecture Notes in Computer Science, vol 4445, pp 193-204, DOI doi:10.1007/978- 3- 540- 71605- 1 18Google Scholar
  313. 313.
    Poli R, Woodward J, Burke E (2007b) A histogram-matching approach to the evolution of bin-packing strategies. In: Proceedings of the IEEE Congress on Evolutionary Computation, Singapore, acceptedGoogle Scholar
  314. 314.
    Potter MA 1997 The design and analysis of a computational model of cooperative coevolution. PhD thesis, George Mason University, Washington, DC, URL http://www.cs.gmu.edu/˜mpotter/dissertation.html Google Scholar
  315. 315.
    Priesterjahn S, Kramer O, Weimer A, Goebels A 2006 Evolution of human-competitive agents in modern computer games. In: Yen GG, Lucas SM, Fogel G, Kendall G, Salomon R, Zhang BT, Coello CAC, Runarsson TP (eds) Pro-ceedings of the 2006 IEEE Congress on Evolutionary Computation, IEEE Press, Vancouver, BC, Canada, pp 777-784, URL http://ieeexplore.ieee.org/servlet/opac?punumber=11108 Google Scholar
  316. 316.
    Prügel-Bennett A, Shapiro JL 1994 An analysis of genetic algorithms using statistical mechanics. Physical Review Letters 72:1305-1309Google Scholar
  317. 317.
    Quintana MI, Poli R, Claridge E 2006 Morphological algorithm design for binary images using genetic programming. Genetic Programming and Evolv-able Machines 7(1):81-102, DOI doi:10.1007/s10710- 006- 7012- 3, URL cshttp://www.essex.ac.uk/staff/rpoli/papers/gpem2005.pdf Google Scholar
  318. 318.
    Ratle A, Sebag M (2000) Genetic programming and domain knowledge: Beyond the limitations of grammar-guided machine discovery. In: Schoenauer M, Deb K, Rudolph G, Yao X, Lutton E, Merelo JJ, Schwefel HP (eds) Parallel Problem Solving from Nature - PPSN VI 6th International Conference, Springer Verlag, Paris, France, LNCS, vol 1917, pp 211-220, URL http://www.lri.fr/˜sebag/REF/PPSN00.ps Google Scholar
  319. 319.
    Reggia J, Tagamets M, Contreras-Vidal J, Jacobs D, Weems S, Naqvi W, Winder R, Chabuk T, Jung J, Yang C (2006) Development of a large-scale integrated neurocognitive architecture - part 2: Design and architecture. Tech. Rep. TR-CS-4827, UMIACS-TR-2006-43, University of Maryland, USA, URL https://drum.umd.edu/dspace/bitstream/1903/3957/1/MarylandPart2.pdf
  320. 320.
    Reif DM, White BC, Moore JH 2004 Integrated analysis of genetic, genomic, and proteomic data. Expert Review of Proteomics 1(1):67-75, DOI doi:10. 1586/14789450.1.1.67, URL http://www.future-drugs.com/doi/abs/10.1586/14789450.1.1.67 Google Scholar
  321. 321.
    Reynolds CW 1987 Flocks, herds, and schools: A distributed behavioral model. SIGGRAPH Computer Graphics 21(4):25-34, URL http://www.red3d.com/cwr/papers/1987/boids.html MathSciNetGoogle Scholar
  322. 322.
    Riolo RL, Worzel B 2003 Genetic Programming Theory and Practice, Genetic Programming, vol 6. Kluwer, Boston, MA, USA, URL http://www.wkap.nl/prod/b/1-4020-7581-2, series Editor - John KozaGoogle Scholar
  323. 323.
    Riolo RL, Soule T, Worzel B(eds)(2007a) Genetic Programming Theory and Practice IV, Genetic and Evolutionary Computation, vol5, Springer, Ann Arbor, URL http://www.springer.com/west/home/computer/foundations?SGWID=%4-156-22-173660377-0 Google Scholar
  324. 324.
    Riolo RL, Soule T, Worzel B (eds) 2007b Genetic Programming Theory and Practice V, Genetic and Evolutionary Computation, Springer, Ann ArborGoogle Scholar
  325. 325.
    Ritchie MD, White BC, Parker JS, Hahn LW, Moore JH (2003) Optimization of neural network architecture using genetic programming improves detec-tion and modeling of gene-gene interactions in studies of human diseases. BMC Bioinformatics 4(28), DOI doi:10.1186/1471- 2105- 4- 28, URL http://www.biomedcentral.com/1471-2105/4/28
  326. 326.
    Ritchie MD, Motsinger AA, Bush WS, Coffey CS, Moore JH 2007 Genetic programming neural networks: A powerful bioinformatics tool for human genetics. Applied Soft Computing 7(1):471-479, DOI doi:10.1016/j.asoc.2006. 01.013Google Scholar
  327. 327.
    Rivero D, nal JRR, Dorado J, Pazos A 2004 Using genetic programming for character discrimination in damaged documents. In: Raidl GR, Cagnoni S, Branke J, Corne DW, Drechsler R, Jin Y, Johnson CR, Machado P, Marchiori E, Rothlauf F, Smith GD, Squillero G (eds) Applications of Evolutionary Computing, EvoWorkshops2004: EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoMUSART, EvoSTOC, Springer Verlag, Coimbra, Portugal, LNCS, vol 3005, pp 349-358Google Scholar
  328. 328.
    Robinson A, Spector L 2002 Using genetic programming with multiple data types and automatic modularization to evolve decentralized and coordinated navigation in multi-agent systems. In: Cantú-Paz E (ed) Late Breaking Papers at the Genetic and Evolutionary Computation Conference (GECCO-2002), AAAI, New York, NY, pp 391-396Google Scholar
  329. 329.
    Rodriguez-Vazquez K, Fonseca CM, Fleming PJ 2004 Identifying the structure of nonlinear dynamic systems using multiobjective genetic programming. IEEE Transactions on Systems, Man and Cybernetics, Part A 34(4):531-545Google Scholar
  330. 330.
    Rosca JP 1997 Analysis of complexity drift in genetic programming. In: Koza JR, Deb K, Dorigo M, Fogel DB, Garzon M, Iba H, Riolo RL (eds) Genetic Programming 1997: Proceedings of the Second Annual Conference, Morgan Kaufmann, Stanford University, CA, USA, pp 286-294, URL ftp://ftp.cs.rochester.edu/pub/u/rosca/gp/97.gp.ps.gzGoogle Scholar
  331. 331.
    Rosca JP, Ballard DH 1996 Discovery of subroutines in genetic programming. In: Angeline PJ, Kinnear, Jr KE (eds) Advances in Genetic Programming 2, MIT Press, Cambridge, MA, USA, chap 9, pp 177-202, URL ftp://ftp.cs. rochester.edu/pub/u/rosca/gp/96.aigp2.dsgp.ps.gzGoogle Scholar
  332. 332.
    Ross BJ, Gualtieri AG, Fueten F, Budkewitsch P 2005 Hyperspectral image analysis using genetic programming. Applied Soft Computing 5(2):147-156, DOI doi:10.1016/j.asoc.2004.06.003, URL http://www.cosc.brocku.ca/˜bross/research/gp_hyper.pdf Google Scholar
  333. 333.
    Rothlauf F (2006) Representations for genetic and evolutionary algorithms, 2nd edn. Springer-Verlag, pub-SV:adr, URL http://download-ebook.org/index. php?target=desc&ebookid=5771, first published 2002, 2nd edition available electronically
  334. 334.
    Ryan C (1999) Automatic Re-engineering of Software Using Genetic Programming, Genetic Programming, vol 2. Kluwer Academic Publishers, URL http://www.wkap.nl/book.htm/0-7923-8653-1
  335. 335.
    Ryan C, Ivan L 1999 An automatice software re-engineering tool based on genetic programming. In: Spector L, Langdon WB, O’Reilly UM, Angeline PJ (eds) Advances in Genetic Programming 3, MIT Press, Cambridge, MA, USA, Ann Arbor, URL http://www.cs.bham.ac.uk/ Google Scholar
  336. 336.
    Ryan C, Collins JJ, O’Neill M (1998) Grammatical evolution: Evolving pro-grams for an arbitrary language. In: Banzhaf W, Poli R, Schoenauer M, Fogarty TC (eds) Proceedings of the First European Workshop on Genetic Program-ming, Springer-Verlag, Paris, LNCS, vol 1391, pp 83-95, URL http://www.lania.mx/˜ccoello/eurogp98.ps.gz Google Scholar
  337. 337.
    Samuel AL (1983) AI, where it has been and where it is going. In: IJCAI, pp 1152-1157Google Scholar
  338. 338.
    Schmidt MD, Lipson H 2006 Co-evolving fitness predictors for accelerating and reducing evaluations. In: Riolo RL, Soule T, Worzel B (eds) Genetic Programming Theory and Practice IV, Genetic and Evolutionary Computation, vol 5, Springer, Ann ArborGoogle Scholar
  339. 339.
    Schoenauer M, Sebag M 2001 Using domain knowledge in evolutionary system identification. In: Giannakoglou KC, Tsahalis D, Periaux J, Papailiou K, Fogarty TC (eds) Evolutionary Methods for Design, Optimization and Control with Applications to Industrial Problems, AthensGoogle Scholar
  340. 340.
    Schoenauer M, Lamy B, Jouve F (1995) Identification of mechanical behavior by genetic programming part II: Energy formulation. Tech. rep., Ecole Polytechnique, 91128 Palaiseau, FranceGoogle Scholar
  341. 341.
    Schoenauer M, Sebag M, Jouve F, Lamy B, Maitournam H 1996 Evolutionary identification of macro-mechanical models. In: Angeline PJ, Kinnear, Jr KE (eds) Advances in Genetic Programming 2, MIT Press, Cambridge, MA, USA, chap 23, pp 467-488, URL http://citeseer.ist.psu.edu/cache/papers/cs/902/ http:zSzzSzwww.eeaax.polytechnique.frzSzpaperszSzmarczSzAGP2.pdf/schoenauer96evolutionary.pdf Google Scholar
  342. 342.
    Searson DP, Montague GA, Willis MJ (1998) Evolutionary design of process controllers. In: In Proceedings of the 1998 United Kingdom Automatic Control Council International Conference on Control (UKACC International Confer-ence on Control ’98), Institution of Electrical Engineers (IEE), University of Wales, Swansea, UK, IEE Conference Publications, vol 455, URL http://www.staff.ncl.ac.uk/d.p.searson/docs/Searsoncontrol98.pdf
  343. 343.
    Sekanina L (2003) Evolvable Components: From Theory to Hardware Implementations. Natural Computing, Springer-Verlag, URL http://www.fit.vutbr. cz/˜sekanina/rehw/books.html.en
  344. 344.
    Setzkorn C 2005 On the use of multi-objective evolutionary algorithms for classification rule induction. PhD thesis, University of Liverpool, UKGoogle Scholar
  345. 345.
    Shah SC, Kusiak A 2004 Data mining and genetic algorithm based gene/SNP selection. Artificial Intelligence in Medicine 31(3):183-196, DOI doi:10.1016/j.artmed.2004.04.002, URL http://www.icaen.uiowa.edu/˜ankusiak/Journal-papers/Gen_Shital.pdf Google Scholar
  346. 346.
    Sharabi S, Sipper M 2006 GP-sumo: Using genetic programming to evolve sumobots. Genetic Programming and Evolvable Machines 7(3):211-230, DOI doi:10.1007/s10710- 006- 9006- 6Google Scholar
  347. 347.
    Sharman KC, Esparcia-Alcazar AI (1993) Genetic evolution of symbolic signal models. In: Proceedings of the Second International Conference on Natural Algorithms in Signal Processing, NASP’93, Essex University, UK, URL http://www.iti.upv.es/˜anna/papers/natalg93.ps
  348. 348.
    Sharman KC, Esparcia Alcazar AI, Li Y 1995 Evolving signal processing algorithms by genetic programming. In: Zalzala AMS (ed) First International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, GALESIA, IEE, Sheffield, UK, vol 414, pp 473-480, URL http://www.iti.upv.es/˜anna/papers/galesi95.ps Google Scholar
  349. 349.
    Shaw AD, Winson MK, Woodward AM, McGovern AC, Davey HM, Kaderbhai N, Broadhurst D, Gilbert RJ, Taylor J, Timmins EM, Goodacre R, Kell DB, Alsberg BK, Rowland JJ (2000) Bioanalysis and biosensors for bioprocess monitoring rapid analysis of high-dimensional bioprocesses using multivari-ate spectroscopies and advanced chemometrics. Advances in Biochemical Engineering/Biotechnology66:83-113, URL http://www.springerlink.com/link.asp?id=t8b4ya0bl42jnjj3
  350. 350.
    Shichel Y, Ziserman E, Sipper M 2005 GP-robocode: Using genetic programming to evolve robocode players. In: Keijzer M, Tettamanzi A, Collet P, van Hemert JI, Tomassini M (eds) Proceedings of the 8th European Con-ference on Genetic Programming, Springer, Lausanne, Switzerland, Lecture Notes in Computer Science, vol 3447, pp 143-154, URL http://www.cs.bgu.ac.il/˜sipper/papabs/eurogprobo-final.pdf Google Scholar
  351. 351.
    Si HZ, Wang T, Zhang KJ, Hu ZD, Fan BT 2006 QSAR study of 1,4-dihydropyridine calcium channel antagonists based on gene expression pro-gramming. Bioorganic & Medicinal Chemistry 14(14):4834-4841, DOI doi: 10.1016/j.bmc.2006.03.019Google Scholar
  352. 352.
    Siegel EV (1994) Competitively evolving decision trees against fixed training cases for natural language processing. In: Kinnear, Jr KE (ed) Advances in Genetic Programming, MIT Press, chap 19, pp 409-423, URL http://www1. cs.columbia.edu/nlp/papers/1994/siegel 94.pdf
  353. 353.
    Sims K 1991 Artificial evolution for computer graphics. ACM Com-puter Graphics 25(4):319-328, URL http://delivery.acm.org/10.1145/130000/122752/p319-sims.pdf, sIGGRAPH ’91 ProceedingsMathSciNetGoogle Scholar
  354. 354.
    Smart W, Zhang M 2004 Applying online gradient descent search to genetic programming for object recognition. In: Hogan J, Montague P, Purvis M, Steke-tee C (eds) CRPIT ’04: Proceedings of the second workshop on Australasian information security, Data Mining and Web Intelligence, and Software Internationalisation, Australian Computer Society, Inc., Dunedin, New Zealand, vol 32 no. 7, pp 133-138, URL http://crpit.com/confpapers/CRPITV32Smart.pdf Google Scholar
  355. 355.
    Soule T, Foster JA 1998a Effects of code growth and parsimony pressure on populations in genetic programming. Evolutionary Computation 6(4):293-309, URL http://mitpress.mit.edu/journals/EVCO/Soule.pdf Google Scholar
  356. 356.
    Soule T, Foster JA (1998b) Removal bias: a new cause of code growth in tree based evolutionary programming. In: 1998 IEEE International Conference on Evolutionary Computation, IEEE Press, Anchorage, Alaska, USA, pp 781-186, URL http://citeseer.ist.psu.edu/313655.html
  357. 357.
    Spector L 2001 Autoconstructive evolution: Push, pushGP, and pushpop. In: Spector L, Goodman ED, Wu A, Langdon WB, Voigt HM, Gen M, Sen S, Dorigo M, Pezeshk S, Garzon MH, Burke E (eds) Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001), Morgan Kauf-mann, San Francisco, California, USA, pp 137-146, URL http://hampshire.edu/lspector/pubs/ace.pdf Google Scholar
  358. 358.
    Spector L 2004 Automatic Quantum Computer Programming: A Genetic Programming Approach, Genetic Programming, vol 7. Kluwer Academic Publishers, Boston/Dordrecht/New York/London, URL http://www.wkap.nl/prod/b/1-4020-7894-3 Google Scholar
  359. 359.
    Spector L, Alpern A (1994) Criticism, culture, and the automatic generation of artworks. In: Proceedings of Twelfth National Conference on Artificial Intelligence, AAAI Press/MIT Press, Seattle, Washington, USA, pp 3-8Google Scholar
  360. 360.
    Spector L, Alpern A (1995) Induction and recapitulation of deep musical structure. In: Proceedings of International Joint Conference on Artificial Intel-ligence, IJCAI’95 Workshop on Music and AI, Montreal, Quebec, Canada, URL http://hampshire.edu/lspector/pubs/IJCAI95mus-toappear.ps
  361. 361.
    Spector L, Barnum H, Bernstein HJ 1998 Genetic programming for quantum computers. In: Koza JR, Banzhaf W, Chellapilla K, Deb K, Dorigo M, Fogel DB, Garzon MH, Goldberg DE, Iba H, Riolo R (eds) Genetic Program-ming 1998: Proceedings of the Third Annual Conference, Morgan Kaufmann, University of Wisconsin, Madison, Wisconsin, USA, pp 365-373Google Scholar
  362. 362.
    Spector L, Barnum H, Bernstein HJ, Swamy N 1999a Finding a betterthan-classical quantum AND/OR algorithm using genetic programming. In: Angeline PJ, Michalewicz Z, Schoenauer M, Yao X, Zalzala A (eds) Proceed-ings of the Congress on Evolutionary Computation, IEEE Press, Mayflower Hotel, Washington D.C., USA, vol 3, pp 2239-2246, URL http://hampshire.edu/˜lasCCS/pubs/spector-cec99.ps Google Scholar
  363. 363.
    Spector L, Langdon WB, O’Reilly UM, Angeline PJ (eds) 1999b Advances in Genetic Programming 3. MIT Press, Cambridge, MA, USA, URL http://www.cs.bham.ac.uk/˜wbl/aigp3 Google Scholar
  364. 364.
    Spector L, Klein J, Keijzer M 2005 The push3 execution stack and the evolution of control. In: Beyer HG, O’Reilly UM, Arnold DV, Banzhaf W, Blum C, Bonabeau EW, Cantu-Paz E, Dasgupta D, Deb K, Foster JA, de Jong ED, Lipson H, Llora X, Mancoridis S, Pelikan M, Raidl GR, Soule T, Tyrrell AM, Watson JP, Zitzler E (eds) GECCO 2005: Proceedings of the 2005 conference on Genetic and evolutionary computation, ACM Press, Washington DC, USA, vol 2, pp 1689-1696, URL http://www.cs.bham.ac.uk/˜wbl/biblio/gecco2005/docs/p1689.pdf Google Scholar
  365. 365.
    Stender J (ed) (1993) Parallel Genetic Algorithms: Theory and Applications. IOS pressGoogle Scholar
  366. 366.
    Stephens CR, Waelbroeck H 1997 Effective degrees of freedom in genetic algorithms and the block hypothesis. In: Bäck T (ed) Proceedings of the Seventh International Conference on Genetic Algorithms (ICGA97), Morgan Kaufmann, East Lansing, pp 34-40Google Scholar
  367. 367.
    Stephens CR, Waelbroeck H 1999 Schemata evolution and building blocks. Evolutionary Computation 7(2):109-124Google Scholar
  368. 368.
    Sterling T 1998 Beowulf-class clustered computing: Harnessing the power of parallelism in a pile of PCs. In: Koza JR, Banzhaf W, Chellapilla K, Deb K, Dorigo M, Fogel DB, Garzon MH, Goldberg DE, Iba H, Riolo R (eds) Genetic Programming 1998: Proceedings of the Third Annual Conference, Mor-gan Kaufmann, University of Wisconsin, Madison, Wisconsin, USA, p 883, invited talkGoogle Scholar
  369. 369.
    Szymanski JJ, Brumby SP, Pope P, Eads D, Esch-Mosher D, Galassi M, Harvey NR, McCulloch HDW, Perkins SJ, Porter R, Theiler J, Young AC, Bloch JJ, David N (2002) Feature extraction from multiple data sources using genetic programming. In: Shen SS, Lewis PE (eds) Algorithms and Tech-nologies for Multispectral, Hyperspectral, and Ultraspectral Imagery VIII, SPIE, vol 4725, pp 338-345, URL http://www.cs.rit.edu/˜dre9227/papers/szymanskiSPIE4725.pdf
  370. 370.
    Tackett WA(1993) Genetic generation of“dendritic” trees for image classification. In: Proceedings of WCNN93, IEEE Press, pp IV646-649, URL http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/ftp.io.com/papers/GP.feature.discovery.ps.Z
  371. 371.
    Takagi H 2001 Interactive evolutionary computation: Fusion of the capabilities of EC optimization and human evaluation. Proceedings of the IEEE 89(9):1275-1296, invited PaperGoogle Scholar
  372. 372.
    Tanev I, Uozumi T, Akhmetov D 2004 Component object based single system image for dependable implementation of genetic programming on clusters. Cluster Computing Journal 7(4):347-356, DOI doi:10.1023/B:CLUS. 0000039494.39217.c1, URL http://www.kluweronline.com/issn/1386-7857 Google Scholar
  373. 373.
    Taylor J, Goodacre R, Wade WG, Rowland JJ, Kell DB 1998 The decon-volution of pyrolysis mass spectra using genetic programming: application to the identification of some eubacterium species. FEMS Microbiology Letters 160:237-246, DOI doi:10.1016/S0378- 1097(98)00038-XGoogle Scholar
  374. 374.
    Teller A 1994 Genetic programming, indexed memory, the halting problem, and other curiosities. In: Proceedings of the 7th annual Florida Artificial Intelli-gence Research Symposium, IEEE Press, Pensacola, Florida, USA, pp 270-274, URL http://www.cs.cmu.edu/afs/cs/usr/astro/public/papers/Curiosities.ps Google Scholar
  375. 375.
    Teller A 1996 Evolving programmers: The co-evolution of intelligent recombi-nation operators. In: Angeline PJ, Kinnear, Jr KE (eds) Advances in Genetic Programming 2, MIT Press, Cambridge, MA, USA, chap 3, pp 45-68, URL http://www.cs.cmu.edu/afs/cs/usr/astro/public/papers/AiGPII.ps Google Scholar
  376. 376.
    Teller A, Andre D 1997 Automatically choosing the number of fitness cases: The rational allocation of trials. In: Koza JR, Deb K, Dorigo M, Fogel DB, Garzon M, Iba H, Riolo RL (eds) Genetic Programming 1997: Proceedings of the Second Annual Conference, Morgan Kaufmann, Stanford University, CA, USA, pp 321-328, URL http://www.cs.cmu.edu/afs/cs/usr/astro/public/papers/GR.ps Google Scholar
  377. 377.
    Teredesai A, Govindaraju V 2005 GP-based secondary classifiers. Pattern Recognition 38(4):505-512, DOI doi:10.1016/j.patcog.2004.06.010Google Scholar
  378. 378.
    Theiler JP, Harvey NR, Brumby SP, Szymanski JJ, Alferink S, Perkins SJ, Porter RB, Bloch JJ (1999) Evolving retrieval algorithms with a genetic pro-gramming scheme. In: Descour MR, Shen SS (eds) Proceedings of SPIE 3753 Imaging Spectrometry V, pp 416-425, URL http://public.lanl.gov/jt/Papers/ga-spie.ps
  379. 379.
    Todd PM, Werner GM(1999) Frankensteinian approaches to evolutionary music composition. In: Griffith N, Todd PM(eds) Musical Networks: Parallel Distributed Perception and Performance, MIT Press, pp 313-340URL http://www-abc.mpib-berlin.mpg.de/users/ptodd/publications/99evmus/99evmus.pdf
  380. 380.
    Tomassini M, Luthi L, Giacobini M, Langdon WB 2007 The structure of the genetic programming collaboration network. Genetic Programming and Evolvable Machines 8(1):97-103, DOI doi:10.1007/s10710- 006- 9018- 2Google Scholar
  381. 381.
    Trujillo L, Olague G 2006a Synthesis of interest point detectors through genetic programming. In: Keijzer M, Cattolico M, Arnold D, Babovic V, Blum C, Bosman P, Butz MV, Coello Coello C, Dasgupta D, Ficici SG, Foster J, Hernandez-Aguirre A, Hornby G, Lipson H, McMinn P, Moore J, Raidl G, Rothlauf F, Ryan C, Thierens D (eds) GECCO 2006: Proceedings of the 8th annual conference on Genetic and evolutionary computation, ACM Press, Seattle, Washington, USA, vol 1, pp 887-894, DOI doi:10.1145/1143997.1144151, URL http://www.cs.bham.ac.uk/˜wbl/biblio/gecco2006/docs/p887.pdf Google Scholar
  382. 382.
    Trujillo L, Olague G (2006b) Using evolution to learn how to perform interest point detection. In: et al XYT (ed) ICPR 2006 18th International Conference on Pattern Recognition, IEEE, vol 1, pp 211-214, DOI doi:10.1109/ICPR. 2006.1153, URL http://www.genetic-programming.org/hc2006/Olague-Paper-2-ICPR%-2006.pdf
  383. 383.
    Tsang EPK, Li J, Butler JM(1998) EDDIE beats the bookies. Software: Practice and Experience 28 (10):1033-1043, DOI doi:10.1002/(SICI)1097- 024X(199808)28:10〈1033::AID-SPE198〉 3.0.CO;2- - 1, URL cshttp://www.essex.ac.uk/CSP/finance/papers/TsBuLi-Eddie-Software98.pdf Google Scholar
  384. 384.
    Turing AM (1948) Intelligent machinery, report for National Physical Labora-tory. Reprinted in Ince, D. C. (editor). 1992. Mechanical Intelligence: Collected Works of A. M. Turing. Amsterdam: North Holland. Pages 107127 . Also reprinted in Meltzer, B. and Michie, D. (editors). 1969. Machine Intelligence r5. Edinburgh: Edinburgh University PressGoogle Scholar
  385. 385.
    Turing AM (1950) Computing machinery and intelligence. Mind 49:433-460, URL http://www.cs.umbc.edu/471/papers/turing.pdf MathSciNetGoogle Scholar
  386. 386.
    Usman I, Khan A, Chamlawi R, Majid A 2007 Image authenticity and perceptual optimization via genetic algorithm and a dependence neighbor-hood. International Journal of Applied Mathematics and Computer Sciences 4 (1):615-620, URL http://www.waset.org/ijamcs/v4/v4-1-7.pdf Google Scholar
  387. 387.
    Vaidyanathan S, Broadhurst DI, Kell DB, Goodacre R 2003 Explanatory optimization of protein mass spectrometry via genetic search. Analyt-ical Chemistry 75(23):6679-6686, DOI doi:10.1021/ac034669a, URL http://dbkgroup.org/Papers/AnalChem75(6679-6686).pdf Google Scholar
  388. 388.
    Venkatraman V, Dalby AR, Yang ZR 2004 Evaluation of mutual information and genetic programming for feature selection in QSAR. Journal of Chemical Information and Modeling 44(5):1686-1692, DOI doi:10.1021/ci049933vGoogle Scholar
  389. 389.
    Vowk B, Wait AS, Schmidt C 2004 An evolutionary approach generates human competitive coreware programs. In: Bedau M, Husbands P, Hutton T, Kumar S, Sizuki H (eds) Workshop and Tutorial Proceedings Ninth Inter-national Conference on the Simulation and Synthesis of Living Systems(Alife XI), Boston, Massachusetts, pp 33-36, artificial Chemistry and its applications workshopGoogle Scholar
  390. 390.
    Vukusic I, Grellscheid SN, Wiehe T 2007 Applying genetic programming to the prediction of alternative mRNA splice variants. Genomics 89(4):471-479, DOI doi:10.1016/j.ygeno.2007.01.001Google Scholar
  391. 391.
    Walker RL 2001 Search engine case study: searching the web using genetic programming and MPI. Parallel Computing 27(1-2):71-89, URL http://www.sciencedirect.com/science/article/B6V12-42K5HNX-4/1/57eb870c72fb7768_bb7d824557444b72 zbMATHGoogle Scholar
  392. 392.
    Walsh P, Ryan C 1996 Paragen: A novel technique for the autoparallelisation of sequential programs using genetic programming. In: Koza JR, Goldberg DE, Fogel DB, Riolo RL (eds) Genetic Programming 1996: Proceedings of the First Annual Conference, MIT Press, Stanford University, CA, USA, pp 406-409, URL http://cognet.mit.edu/library/books/view?isbn=0262611279 Google Scholar
  393. 393.
    Weaver DC (2004) Applying data mining techniques to library design, lead generation and lead optimization. Current Opinion in Chemical Biology 8(3):264-270, DOI doi:10.1016/j.cbpa.2004.04.005, URL http://www.sciencedirect.com/science/article/B6VRX-4CB69R1-2/2/84a354cec9064ed07baab6a07998c942 Google Scholar
  394. 394.
    Whigham PA (1995) A schema theorem for context-free grammars. In: 1995 IEEE Conference on Evolutionary Computation, IEEE Press, Perth, Australia, vol 1, pp 178-181Google Scholar
  395. 395.
    Whigham PA 1996 Search bias, language bias, and genetic programming. In: Koza JR, Goldberg DE, Fogel DB, Riolo RL (eds) Genetic Programming 1996: Proceedings of the First Annual Conference, MIT Press, Stanford University, CA, USA, pp 230-237Google Scholar
  396. 396.
    Whitley D 2001 An overview of evolutionary algorithms: practical issues and common pitfalls. Information and Software Technology 43(14):817-831, DOI doi:10.1016/S0950- 5849(01)00188- 4, URL http://www.cs.colostate.edu/˜genitor/2001/overview.pdf Google Scholar
  397. 397.
    Whitley LD 1994 A Genetic Algorithm Tutorial. Statistics and Computing 4:65-85Google Scholar
  398. 398.
    Willis M, Hiden H, Marenbach P, McKay B, Montague GA 1997a Genetic programming: An introduction and survey of applications. In: Zalzala A (ed) Second International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, GALESIA, Institution of Electrical Engineers, University of Strathclyde, Glasgow, UK, URL http://www.staff.ncl.ac.uk/d.p.searson/docs/galesia97surveyofGP.pdf Google Scholar
  399. 399.
    Willis MJ, Hiden HG, Montague GA (1997b) Developing inferential estima-tion algorithms using genetic programming. In: IFAC/ADCHEM International Symposium on Advanced Control of Chemical Processes, Banaff, Canada, pp 219-224Google Scholar
  400. 400.
    Wilson G, Heywood M(2007) Introducing probabilistic adaptive mapping developmental genetic programming with redundant mappings. Genetic Programming and Evolvable Machines 8(2):187-220, DOI doi:10.1007/s10710- 007- 9027- 9, special issue on developmental systemsGoogle Scholar
  401. 401.
    Wong ML 1998 An adaptive knowledge-acquisition system using generic genetic programming. Expert Systems with Applications 15(1):47-58, URL http://cptra.ln.edu.hk/˜mlwong/journal/esa1998.pdf Google Scholar
  402. 402.
    Wong ML 2005 Evolving recursive programs by using adaptive grammar based genetic programming. Genetic Programming and Evolvable Machines 6 (4):421-455, DOI doi:10.1007/s10710- 005- 4805- 8, URL http://cptra.ln.edu.hk/˜mlwong/journal/gpem2005.pdf Google Scholar
  403. 403.
    Wong ML, Leung KS 1995 Inducing logic programs with genetic algorithms: the genetic logicprogramming system genetic logic programming and applications. IEEE Expert 10(5):68-76, DOI doi:10.1109/64.464935Google Scholar
  404. 404.
    Wong ML, Leung KS 1996 Evolving recursive functions for the even-parity problem using genetic programming. In: Angeline PJ, Kinnear, Jr KE (eds) Advances in Genetic Programming 2, MIT Press, Cambridge, MA, USA, chap 11, pp 221-240Google Scholar
  405. 405.
    Wong ML, Leung KS (2000) Data Mining Using Grammar Based Genetic Programming and Applications, Genetic Programming, vol 3. Kluwer Academic PublishersGoogle Scholar
  406. 406.
    Wong ML, Wong TT, Fok KL 2005 Parallel evolutionary algorithms on graphics processing unit. In: Corne D, Michalewicz Z, McKay B, Eiben G, Fogel D, Fonseca C, Greenwood G, Raidl G, Tan KC, Zalzala A (eds) Proceed-ings of the 2005 IEEE Congress on Evolutionary Computation, IEEE Press, Edinburgh, Scotland, UK, vol 3, pp 2286-2293, URL http://ieeexplore.ieee.org/servlet/opac?punumber=10417&isvol=3 Google Scholar
  407. 407.
    Woodward AM, Gilbert RJ, Kell DB 1999 Genetic programming as an analytical tool for non-linear dielectric spectroscopy. Bioelectrochemistry and Bioenergetics 48 (2):389-396, DOI doi:doi:10.1016/S0302- 4598(99)00022- 7, URL http://www.sciencedirect.com/science/article/B6TF7-3WJ72RJ-T/2/19fd01a6eb6ae0b8e12b2bb2218fb6e9 Google Scholar
  408. 408.
    Wright S (1932) The roles of mutation, inbreeding, crossbreeding and selection in evolution. In: Jones DF (ed) Proceedings of the Sixth International Congress on Genetics, vol 1, pp 356-366Google Scholar
  409. 409.
    Xie H, Zhang M, Andreae P 2006 Genetic programming for automatic stress detection in spoken english. In: Rothlauf F, Branke J, Cagnoni S, Costa E, Cotta C, Drechsler R, Lutton E, Machado P, Moore JH, Romero J, Smith GD, Squillero G, Takagi H (eds) Applications of Evolutionary Computing, EvoWorkshops2006: EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoInter-action, EvoMUSART, EvoSTOC, Springer Verlag, Budapest, LNCS, vol 3907, pp 460-471, DOI doi:10.1007/11732242 41, URL http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3907&spage=460 Google Scholar
  410. 410.
    Yangiya M (1995) Efficient genetic programming based on binary decision dia-grams. In: 1995 IEEE Conference on Evolutionary Computation, IEEE Press, Perth, Australia, vol 1, pp 234-239Google Scholar
  411. 411.
    Yu J, Bhanu B 2006 Evolutionary feature synthesis for facial expression recognition. Pattern Recognition Letters 27(11):1289-1298, DOI doi:10.1016/j.patrec.2005.07.026, evolutionary Computer Vision and Image UnderstandingGoogle Scholar
  412. 412.
    Yu J, Yu J, Almal AA, Dhanasekaran SM, Ghosh D, Worzel WP, Chinnaiyan AM 2007 Feature selection and molecular classification of cancer using genetic programming. Neoplasia 9(4):292-303, DOI doi:10.1593/neo.07121Google Scholar
  413. 413.
    Yu T 2001 Hierachical processing for evolving recursive and modular programs using higher order functions and lambda abstractions. Genetic Programming and Evolvable Machines 2 (4):345-380, DOI doi:10.1023/A: 1012926821302zbMATHGoogle Scholar
  414. 414.
    Yu T, Chen SH (2004) Using genetic programming with lambda abstraction to find technical trading rules. In: Computing in Economics and Finance, University of AmsterdamGoogle Scholar
  415. 415.
    Yu T, Riolo RL, Worzel B (eds) 2005 Genetic Programming Theory and Practice III, Genetic Programming, vol 9, Springer, Ann ArborGoogle Scholar
  416. 416.
    Zhang BT, Mühlenbein H 1993 Evolving optimal neural networks using genetic algorithms with Occam’s razor. Complex Systems 7:199-220, URL http://citeseer.ist.psu.edu/zhang93evolving.html Google Scholar
  417. 417.
    Zhang BT, Mühlenbein H 1995 Balancing accuracy and parsimony in genetic programming. Evolutionary Computation 3(1):17-38, URL http://www.ais.fraunhofer.de/˜muehlen/publications/gmd_as_ga-94_09.ps Google Scholar
  418. 418.
    Zhang BT, Ohm P, Mühlenbein H 1997 Evolutionary induction of sparse neural trees. Evolutionary Computation 5(2):213-236, URL http://bi.snu.ac.kr/Publications/Journals/International/EC5-2.ps zbMATHGoogle Scholar
  419. 419.
    Zhang M, Smart W 2006 Using gaussian distribution to construct fitness functions in genetic programming for multiclass object classification. Pattern Recognition Letters 27(11):1266-1274, DOI doi:10.1016/j.patrec.2005.07.024, evolutionary Computer Vision and Image UnderstandingGoogle Scholar
  420. 420.
    Zhang Y, Rockett PI (2006) Feature extraction using multi-objective genetic programming. In: Jin Y (ed) Multi-Objective Machine Learning, Studies in Computational Intelligence, vol 16, Springer, chap 4, pp 79-106, invited chapterGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • William B. Langdon
    • 1
  • Riccardo Poli
    • 2
  • Nicholas F. McPhee
    • 3
  • John R. Koza
    • 4
  1. 1.Departments of Biological and Mathematical SciencesUniversity of EssexUK
  2. 2.Department of Computing and Electronic SystemsUniversity of EssexUK
  3. 3.Division of Science and MathematicsUniversity of MinnesotaMorrisUSA
  4. 4.Stanford UniversityStanfordUSA

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