Page 195 - 49A Field Guide to Genetic Programming

BIBLIOGRAPHY 181

2002. Morgan Kaufmann Publishers. ISBN 1-55860-878-8. URL http://www.cs.bham.
ac.uk/~wbl/biblio/gecco2002/GP272.pdf. GPBiB
C. Gagne and M. Parizeau. Genetic engineering of hierarchical fuzzy regional represen-
tations for handwritten character recognition. International Journal on Document
Analysis and Recognition, 8(4):223–231, September 2006. URL http://vision.gel.
ulaval.ca/fr/publications/Id_607/PublDetails.php. GPBiB
C. Gagn´e and M. Parizeau. Co-evolution of nearest neighbor classifiers. International
Journal of Pattern Recognition and Artificial Intelligence, 21(5):921–946, August
2007. ISSN 0218-0014. URL http://vision.gel.ulaval.ca/en/publications/Id_
692/PublDetails.php. GPBiB
A. L. Garcia-Almanza and E. P. K. Tsang. Simplifying decision trees learned by genetic
programming. In Proceedings of the 2006 IEEE Congress on Evolutionary Computa-
tion, pages 7906–7912, Vancouver, 6-21 July 2006. IEEE Press. ISBN 0-7803-9487-9.
URL http://privatewww.essex.ac.uk/~algarc/Publications/WCCI2006.pdf. GPBiB
A. L. Garcia-Almanza and E. P. K. Tsang. Repository method to suit different investment
strategies. In D. Srinivasan and L. Wang, editors, 2007 IEEE Congress on Evolutionary
Computation, pages 790–797, Singapore, 25-28 September 2007. IEEE Computational
Intelligence Society, IEEE Press. ISBN 1-4244-1340-0. GPBiB

C. Gathercole and P. Ross. Dynamic training subset selection for supervised learn-
ing in genetic programming. In Y. Davidor, et al., editors, Parallel Problem Solv-
ing from Nature III, volume 866 of LNCS, pages 312–321, Jerusalem, 9-14 October
1994. Springer-Verlag. ISBN 3-540-58484-6. URL http://citeseer.ist.psu.edu/
gathercole94dynamic.html. GPBiB

C. Gathercole and P. Ross. The MAX problem for genetic programming - highlighting
an adverse interaction between the crossover operator and a restriction on tree depth.
Technical report, Department of Artificial Intelligence, University of Edinburgh, 80
South Bridge, Edinburgh, EH1 1HN, UK, 1995. URL http://citeseer.ist.psu.edu/
gathercole95max.html. GPBiB

C. Gathercole and P. Ross. Tackling the boolean even N parity problem with ge-
netic programming and limited-error fitness. In J. R. Koza, et al., editors, Ge-
netic Programming 1997: Proceedings of the Second Annual Conference, pages 119–
127, Stanford University, CA, USA, 13-16 July 1997. Morgan Kaufmann. URL
http://citeseer.ist.psu.edu/79389.html. GPBiB

S. Gelly, O. Teytaud, N. Bredeche, and M. Schoenauer. Universal consistency and bloat
in GP. Revue d’Intelligence Artificielle, 20(6):805–827, 2006. ISSN 0992-499X. URL
http://hal.inria.fr/docs/00/11/28/40/PDF/riabloat.pdf. Issue on New Methods
in Machine Learning. Theory and Applications. GPBiB

Genetic Programming mailing list, 2001-2008. URL http://tech.groups.yahoo.com/
group/genetic_programming/.
R. J. Gilbert, R. Goodacre, A. M. Woodward, and D. B. Kell. Genetic programming: A
novel method for the quantitative analysis of pyrolysis mass spectral data. ANALYT-
ICAL CHEMISTRY, 69(21):4381–4389, 1997. URL http://pubs.acs.org/journals/
ancham/article.cgi/ancham/1997/69/i21/pdf/ac970460j.pdf. GPBiB

A. Globus, J. Lawton, and T. Wipke. Automatic molecular design using evolutionary
techniques. In A. Globus and D. Srivastava, editors, The Sixth Foresight Conference
100 1000 2
Avg Size Avg Fitness sin(x)
Best Fitness GP (gen=6)
90
1.5
80
1
70
100
0.5
60
Generation 6 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