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Parallel evolution of game evaluation functions in ada
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Annual International Conference on Ada archive
Proceedings of the 2007 ACM international conference on SIGAda annual international conference table of contents
Fairfax, Virginia, USA
SESSION: Conference program table of contents
Pages: 59 - 62  
Year of Publication: 2007
ISBN:978-1-59593-876-3
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Authors
Tyler B. Hallmark  United States Military Academy, West Point, NY
Eugene K. Ressler  United States Military Academy, West Point, NY
Sponsors
ACM: Association for Computing Machinery
SIGADA: ACM Special Interest Group on Ada Programming Language
Publisher
ACM  New York, NY, USA
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ABSTRACT

This is an Ada experience report, where we conclude that Ada tasking and distributed processing facilities make it a good research tool for experimentation with algorithms that might eventually need multiple processors. We implemented a genetic algorithm in Ada to create effective computer players for Connect4. Key to our success was employing Ada tasking and ALRM Annex E Distributed computing to harness a symmetric multiproces-sor and a distributed machine with very few code changes. Easy extension of an original single-task code to multi-tasking and distributed variants-even though extension was not planned in advance-was essential to timely completion. Using either the parallel or distributed implementation, about 150 processor hours were sufficient to evolve players that neither the GNU "Four-in-a-Row" Expert player nor the author could defeat. This algorithm relies on human expertise to restrict the genetic search space. Work is in progress on a new algorithm with near-zero encoded knowledge, which will run on 220 distributed nodes within the same distributed computing framework.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
Fogel, David B. New Results on Evolving Strategies in Chess. Applications and Science of Neural, Fuzzy Systems, and Evolutionary Computation VI 5200 (2003): 56--63.
 
2
Wikipedia contributors, "Connect Four," Wikipedia, The Free Encyclopedia, http://en.wikipedia.org/w/index.php?title=Connect_Four&oldid=131063397 (accessed May 19, 2007).
 
3
Wikipedia contributors, "Connect Four," Wikipedia, The Free Encyclopedia, http://en.wikipedia.org/w/index.php?title=Connect_Four&oldid=131063397 (accessed May 8, 2007).
 
4
 
5
Allis, Victor. A Knowledge-Based Approach to Connect Four. Masters Thesis, Vrije University, Amsterdam, Netherlands, 1988.
 
6
 
7
Author Unknown, "Four-in-a-Row," http://live.gnome.org/Four-in-a-row (accessed May 17, 2007).
 
8
Krischik, Martin, Welcome to Annex E for GNAT, http://gnat-glade.sourceforge.net/pmwiki.php/Main/HomePage.
 
9
Wrigley, Adrian, "Re: Help with Glade (Annex E) on Windows", USENET comp.lang.ada, Jan 29, 2007, 11:29:46 GMT.
 
10
Wikipedia contributors, "Parallel Virtual Machine," Wikipedia, The Free Encyclopedia, http://en.wikipedia.org/wiki/Parallel_Virtual_Machine, (accessed May 8, 2007).

Collaborative Colleagues:
Tyler B. Hallmark: colleagues
Eugene K. Ressler: colleagues