GP and Chess

Since I started reading A Field Guide to Genetic Programming, I discovered that work has been done in the area of Chess and genetic programming. In addition to my previous blog entry discussing GP-Chess, Dr. Ami Hauptman and Dr. Moshe Sipper continue their work in their paper Evolution of an efficient search algorithm for the mate-in-N problem in chess. They continued their work and improved the search efficiency via GP in solving mate-in-N problems. The improvements were very significant since they used Crafty as the baseline measure. I include their results:

Mate-in	1	2	3	4	5
CRAFTY	600	7K	50K	138K	1.6M
Evolved	600	2k	28k	55K	850k

The work is a significant improvement over the previous 40years of chess research.

What does this mean for me? There is plenty of areas of chess yet to investigate.

REFERENCES

A. Hauptman and M. Sipper. GP-endchess: Using genetic programming to evolve chess endgame players. In M. Keijzer, et al., editors, Proceedings of the 8th European Conference on Genetic Programming, volume 3447 of Lecture Notes in Computer Science, pages 120–131, Lausanne, Switzerland, 30 March - 1 April 2005. Springer. ISBN 3-540-25436-6.

A. Hauptman and M. Sipper. Evolution of an efficient search algorithm for the mate-in-N problem in chess. In M. Ebner, et al., editors, Proceedings of the 10th European Conference on Genetic Programming, volume 4445 of Lecture Notes in Computer Science, pages 78–89, Valencia, Spain, 11 - 13 April 2007. Springer. ISBN 3-540-71602-5.

R. Poli, W. B. Langdon, and N. F. McPhee. A field guide to genetic programming. Published via http://lulu.com and freely available at http://www.gp-field-guide.org.uk/, 2008. (With contributions by J. R. Koza).