Page 183 - 49A Field Guide to Genetic Programming

BIBLIOGRAPHY 169

2000. URL http://www.sciencedirect.com/science/article/B6V0H-482MDPD-8/2/
dd470648e2228c84efe7e14ca3841b7e. GPBiB
M. P. Austin, G. Bates, M. A. H. Dempster, V. Leemans, and S. N. Williams.
Adaptive systems for foreign exchange trading. Quantitative Finance, 4(4):37–
45, August 2004. ISSN 1469-7688. URL http://www-cfr.jbs.cam.ac.uk/archive/
PRESENTATIONS/seminars/2006/dempster2.pdf. GPBiB
Y. Azaria and M. Sipper. GP-gammon: Genetically programming backgammon players.
Genetic Programming and Evolvable Machines, 6(3):283–300, September 2005a. ISSN
1389-2576. URL http://www.cs.bgu.ac.il/~sipper/papabs/gpgammon.pdf. Pub-
lished online: 12 August 2005. GPBiB
Y. Azaria and M. Sipper. Using GP-gammon: Using genetic programming to evolve
backgammon players. In M. Keijzer, et al., editors, Proceedings of the 8th Euro-
pean Conference on Genetic Programming, volume 3447 of Lecture Notes in Computer
Science, pages 132–142, Lausanne, Switzerland, 30 March - 1 April 2005b. Springer.
ISBN 3-540-25436-6. URL http://springerlink.metapress.com/openurl.asp?genre=
article&issn=0302-9743&volume=3447&spage=132. GPBiB
V. Babovic. Emergence, evolution, intelligence; Hydroinformatics - A study of distributed
and decentralised computing using intelligent agents. A. A. Balkema Publishers, Rot-
terdam, Holland, 1996. ISBN 90-5410-404-X. GPBiB
M. Bader-El-Den and R. Poli. Generating sat local-search heuristics using a gp hyper-
heuristic framework. In Proceedings of Evolution Artificielle, October 2007a.
M. B. Bader-El-Den and R. Poli. A GP-based hyper-heuristic framework for evolving
3-SAT heuristics. In D. Thierens, et al., editors, GECCO ’07: Proceedings of the 9th
annual conference on Genetic and evolutionary computation, volume 2, pages 1749–
1749, London, 7-11 July 2007b. ACM Press. URL http://www.cs.bham.ac.uk/~wbl/
biblio/gecco2007/docs/p1749.pdf. GPBiB
K. M. S. Badran and P. I. Rockett. The roles of diversity preservation and mutation in
preventing population collapse in multiobjective genetic programming. In D. Thierens,
et al., editors, GECCO ’07: Proceedings of the 9th annual conference on Genetic
and evolutionary computation, volume 2, pages 1551–1558, London, 7-11 July 2007.
ACM Press. URL http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p1551.
pdf. GPBiB
W. Bains, R. Gilbert, L. Sviridenko, J.-M. Gascon, R. Scoffin, K. Birchall, I. Harvey,
and J. Caldwell. Evolutionary computational methods to predict oral bioavailability
QSPRs. Current Opinion in Drug Discovery and Development, 5(1):44–51, January
2002. GPBiB
J. E. Baker. Reducing bias and inefficiency in the selection algorithm. In J. J. Grefenstette,
editor, Proceedings of the Second International Conference on Genetic Algorithms
and their Application, pages 14–21, Cambridge, MA, USA, 1987. Lawrence Erlbaum
Associates. ISBN 0-8058-0158-8.
J. Balic. Flexible Manufacturing Systems; Development - Structure - Operation - Han-
dling - Tooling. Manufacturing technology. DAAAM International, Vienna, 1999. ISBN
3-901509-03-8. GPBiB
S. Baluja and R. Caruana. Removing the genetics from the standard genetic algorithm.
In A. Prieditis and S. Russell, editors, Machine Learning: Proceedings of the Twelfth
International Conference, pages 38–46. Morgan Kaufmann Publishers, San Francisco,
CA, 1995.
100 1000 2
Avg Size Avg Fitness sin(x)
Best Fitness GP (gen=0)
90
1.5
80
1
70
100
0.5
60
Generation 0 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