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BIBLIOGRAPHY                                                  177


            K. Deb, R. Poli, W. Banzhaf, H.-G. Beyer, E. Burke, P. Darwen, D. Dasgupta,
              D. Floreano, J. Foster, M. Harman, O. Holland, P. L. Lanzi, L. Spector, A. Tetta-
              manzi, D. Thierens, and A. Tyrrell, editors. Genetic and Evolutionary Computa-
              tion – GECCO-2004, Part I, volume 3102 of Lecture Notes in Computer Science,
              Seattle, WA, USA, 26-30 June 2004. Springer-Verlag.  ISBN 3-540-22344-4.  URL
              http://www.springerlink.com/content/978-3-540-22344-3.     GPBiB
            M. Defoin Platel, M. Clergue, and P. Collard. Maximum homologous crossover for linear
              genetic programming. In C. Ryan, et al., editors, Genetic Programming, Proceed-
              ings of EuroGP’2003, volume 2610 of LNCS, pages 194–203, Essex, 14-16 April 2003.
              Springer-Verlag.  ISBN 3-540-00971-X.  URL http://www.i3s.unice.fr/~defoin/
              publications/eurogp_03.pdf.                                GPBiB
            M. A. H. Dempster and C. M. Jones. A real-time adaptive trading system using genetic
              programming. Quantitative Finance, 1:397–413, 2000. URL http://mahd-pc.jbs.cam.
              ac.uk/archive/PAPERS/2000/geneticprogramming.pdf.          GPBiB
            M. A. H. Dempster, T. W. Payne, Y. Romahi, and G. W. P. Thompson. Computational
              learning techniques for intraday FX trading using popular technical indicators. IEEE
              Transactions on Neural Networks, 12(4):744–754, July 2001. ISSN 1045-9227. URL
              http://mahd-pc.jbs.cam.ac.uk/archive/PAPERS/2000/ieeetrading.pdf.  GPBiB
            L. Deschaine. 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, May 2006. URL http://www.mining-journal.
              com/gdi_magazine/pdf/GDI0605scr.pdf.                       GPBiB
            L. M. Deschaine, R. A. Hoover, J. N. Skibinski, J. J. Patel, F. Francone, P. Nordin,
              and M. J. Ades. 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, 14-18 April 2002. URL http://www.cs.ucl.ac.uk/staff/W.Langdon/
              ftp/papers/deschaine/ASTC_2002_UXOFinder_Invention_Paper.pdf.  GPBiB
            L. M. Deschaine, J. J. Patel, R. D. Guthrie, J. T. Grimski, and M. J. Ades. Using linear
              genetic programming to develop a C/C++ simulation model of a waste incinerator.
              In M. Ades, editor, Advanced Technology Simulation Conference, Seattle, 22-26 April
              2001. URL http://www.aimlearning.com/Environmental.Engineering.pdf.  GPBiB
            P. D’haeseleer. Context preserving crossover in genetic programming. In Proceedings of
              the 1994 IEEE World Congress on Computational Intelligence, volume 1, pages 256–
              261, Orlando, Florida, USA, 27-29 June 1994. IEEE Press. URL http://www.cs.ucl.
              ac.uk/staff/W.Langdon/ftp/ftp.io.com/papers/WCCI94_CPC.ps.Z.  GPBiB
            P. D’haeseleer and J. Bluming. Effects of locality in individual and population evolution.
              In K. E. Kinnear, Jr., editor, Advances in Genetic Programming, chapter 8, pages
              177–198. MIT Press, 1994. URL http://cognet.mit.edu/library/books/view?isbn=
              0262111888.                                                GPBiB
            S. Dignum and R. Poli. Generalisation of the limiting distribution of program sizes in
              tree-based genetic programming and analysis of its effects on bloat. In D. Thierens,
              et al., editors, GECCO ’07: Proceedings of the 9th annual conference on Genetic
              and evolutionary computation, volume 2, pages 1588–1595, London, 7-11 July 2007.
              ACM Press. URL http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p1588.
              pdf.                                                       GPBiB
                                    100             1000            2
                                              Avg Size       Avg Fitness      sin(x)
                                                             Best Fitness    GP (gen=4)
                                    90
                                                                    1.5
                                    80
                                                                    1
                                    70
                                                    100
                                                                    0.5
                                    60
                      Generation 4  Average Size   50  Fitness      0
                                    40                             -0.5
                                                     10
                                    30
                      (see Sec. B.4)   20                          -1
                                                                   -1.5
                                    10
                                                     1             -2
                                     0   20   40   60   80   100   0   20   40   60   80   100   0   1   2   3   4   5   6
                                         Generations     Generations      x
   186   187   188   189   190   191   192   193   194   195   196