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BIBLIOGRAPHY                                                  201


            R. R. F. Mendes, F. de B. Voznika, J. C. Nievola, and A. A. Freitas. Discovering fuzzy
              classification rules with genetic programming and co-evolution. In L. Spector, et al., ed-
              itors, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-
              2001), page 183, San Francisco, California, USA, 7-11 July 2001. Morgan Kaufmann.
              ISBN 1-55860-774-9. URL http://www.cs.bham.ac.uk/~wbl/biblio/gecco2001/d02.
              pdf.                                                       GPBiB
            P. K. Mercure, G. F. Smits, and A. Kordon.  Empirical emulators for first
              principle models.  In AIChE Fall Annual Meeting, Reno Hilton, 6 Novem-
              ber 2001. URL http://www.aiche.org/conferences/techprogram/paperdetail.asp?
              PaperID=2373&DSN=annual01.                                 GPBiB

            J. Meyer-Spradow and J. Loviscach. Evolutionary design of BRDFs. In M. Chover, et al.,
              editors, Eurographics 2003 Short Paper Proceedings, pages 301–306, 2003. URL http:
              //viscg.uni-muenster.de/publications/2003/ML03/evolutionary_web.pdf. GPBiB
            J. Miller, M. Tomassini, P. L. Lanzi, C. Ryan, A. G. B. Tettamanzi, and W. B. Langdon,
              editors. Genetic Programming, Proceedings of EuroGP’2001, volume 2038 of LNCS,
              Lake Como, Italy, 18-20 April 2001. Springer-Verlag. ISBN 3-540-41899-7. URL http:
              //link.springer.de/link/service/series/0558/tocs/t2038.htm.  GPBiB
            J. F. Miller. An empirical study of the efficiency of learning boolean functions using a
              cartesian genetic programming approach. In W. Banzhaf, et al., editors, Proceedings of
              the Genetic and Evolutionary Computation Conference, volume 2, pages 1135–1142,
              Orlando, Florida, USA, 13-17 July 1999. Morgan Kaufmann. ISBN 1-55860-611-4.
              URL http://citeseer.ist.psu.edu/153431.html.               GPBiB
            J. F. Miller and S. L. Smith. Redundancy and computational efficiency in cartesian genetic
              programming. IEEE Transactions on Evolutionary Computation, 10(2):167–174, April
              2006.                                                      GPBiB
            J. F. Miller, A. Thompson, P. Thomson, and T. C. Fogarty, editors. Proceedings of
              the Third International Conference on Evolvable Systems, ICES 2000, volume 1801
              of LNCS, Edinburgh, Scotland, UK, 17-19 April 2000. Springer-Verlag. ISBN 3-540-
              67338-5.
            B. Mitavskiy and J. Rowe. Some results about the markov chains associated to GPs and
              to general EAs. Theoretical Computer Science, 361(1):72–110, 28 August 2006. GPBiB
            D. J. Montana. Strongly typed genetic programming. Evolutionary Computation, 3(2):
              199–230, 1995. URL http://vishnu.bbn.com/papers/stgp.pdf.  GPBiB
            G. E. Moore.  Cramming more components onto integrated circuits.  Electron-
              ics, 38(8):114–117, 1965.  URL ftp://download.intel.com/museum/Moores_Law/
              Articles-Press_Releases/Gordon_Moore_1965_Article.pdf.

            J. H. Moore, J. S. Parker, N. J. Olsen, and T. M. Aune. Symbolic discriminant analysis
              of microarray data in automimmune disease. Genetic Epidemiology, 23:57–69, 2002.
              GPBiB
            A. A. Motsinger, S. L. Lee, G. Mellick, and M. D. Ritchie. 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, January 25
              2006. ISSN 1471-2105. URL http://www.biomedcentral.com/1471-2105/7/39. GPBiB
            H. M¨uhlenbein and T. Mahnig. Convergence theory and application of the factorized
              distribution algorithm. Journal of Computing and Information Technology, 7(1):19–
              32, 1999a.
                                    100             1000            2
                                              Avg Size       Avg Fitness      sin(x)
                                                             Best Fitness    GP (gen=23)
                                    90
                                                                    1.5
                                    80
                                                                    1
                                    70
                                                    100
                                                                    0.5
                                    60
                      Generation 23  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
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