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Optimizing Fitness Function for the Game of Go-Moku

IP Multimedia Communications
© 2011 by IJCA Journal
ISBN : 978-93-80864-99-3
Year of Publication: 2011
Thaker Chirag S
Dharm Singh
Shah Sanjay M

Thaker Chirag S, Dharm Singh and Shah Sanjay M. Optimizing Fitness Function for the Game of Go-Moku. Special issues on IP Multimedia Communications (1):94-97, October 2011. Full text available. BibTeX

	author = {Thaker Chirag S and Dharm Singh and Shah Sanjay M},
	title = {Optimizing Fitness Function for the Game of Go-Moku},
	journal = {Special issues on IP Multimedia Communications},
	month = {October},
	year = {2011},
	number = {1},
	pages = {94-97},
	note = {Full text available}


Game playing has been the area of research in Artificial intelligence. Particularly, board game playing programs are often described as being a combination of search and knowledge. Board Games, due to its very nature, provide dynamic environments that make them ideal area of computational intelligence theories, architectures, and algorithms. In board games, it has always been the challenging task to build a quality evaluation function. The goodness or badness of the evaluation function is determined by its accuracy, relevance, cost and outcome. All of these parameters must be addressed and the weighed results are added to an evaluation function experimentally. Evolutionary algorithms such as Genetic algorithm are applied to the game playing because of the very large state space of the problem. While following the natural evolution, the fitness of an individual is defined with respect to its competitors and collaborators, as well as to the environment. Evolutionary algorithms follow the same path to evolve game playing programs. Among all computer board games, Go-moku, which is a variant of a Game of GO. This paper mainly highlights how genetic algorithm can be applied to game of Go-Moku.


  1. Hong, J.-H. and Cho, S.-B. (2004). Evolution of emergent behaviors for shooting game characters in robocode. In Evolutionary Computation, 2004. CEC2004. Congress on Evolutionary Computation, volume 1, pages 634–638, Piscataway, NJ. IEEE.
  2. J. Clune. Heuristic evaluation functions for general game playing. In Proc. of AAAI, 1134–1139, 2007.
  3. J¨org Denzinger, Kevin Loose, Darryl Gates, and John Buchanan. Dealing with parameterized actions in behavior testing of commercial computer games. In Proceedings of the IEEE 2005 Symposium on Computational Intelligence and Games (CIG), pages 37–43, 2005.
  4. Matt Gilgenbach. Fun game AI design for beginners. In Steve Rabin, editor, AI Game Programming Wisdom 3, 2006.
  5. S. Schiffel and M. Thielscher. A multiagent semantics for the game description language. In Proc. of the Int.’l Conf. on Agents and Artificial Intelligence, Porto 2009. Springer LNCS.
  6. T. Srinivasan, P.J.S. Srikanth, K. Praveen and L. Harish Subramaniam, “AI Game Playing Approach for Fast Processor Allocation in Hypercube Systems using Veitch diagram (AIPA)”, IADIS International Conference on Applied Computing 2005, vol. 1, Feb. 2005, pp. 65-72.
  7. Thomas P. Runarsson and Simon M. Lucas. Co-evolution versus self-play temporal difference learning for acquiring position evaluation in small-board go. IEEE Transactions on Evolutionary Computation, 9:628 – 640, 2005.
  8. Yannakakis, G., Levine, J., and Hallam, J. (2004). An evolutionary approach for interactive computer games. In Evolutionary Computation, 2004. CEC2004. Congress on Evolutionary Computation, volume 1, pages 986–993, Piscataway, NJ. IEEE.
  9. A. Hauptman and M. Sipper. Evolution of an efficient search algorithm for the Mate-in-N problem in chess. In Proceedings of the 2007 European Conference on Genetic Programming, pages 78–89. Springer, Valencia, Spain, 2007.
  10. P. Aksenov. Genetic algorithms for optimising chess position scoring. Master’s Thesis, University of Joensuu, Finland, 2004. Y. Bjornsson and T.A. Marsland. Multi-cut alpha-beta-pruning in game-tree search. Theoretical Computer Science, 252(1-2):177–196, 2001.
  11. O. David-Tabibi, A. Felner, and N.S. Netanyahu. Blockage detection in pawn endings. Computers and Games CG 2004, eds. H.J. van den Herik, Y. Bjornsson, and N.S. Netanyahu, pages 187–201. Springer-Verlag, 2006.
  12. A. Hauptman and M. Sipper. Using genetic programming to evolve chess endgame players. In Proceedings of the 2005 European Conference onGenetic Programming, pages 120–131. Springer, Lausanne, Switzerland, 2005.
  13. G. Kendall and G. Whitwell. An evolutionary approach for the tuning of a chess evaluation function using population dynamics. In Proceedings of the 2001 Congress on Evolutionary Computation, pages 995–1002. IEEE Press, World Trade Center, Seoul, Korea, 2001.
  14. Holland, J. H. (1975). Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence. Ann Arbor, MI: University of Michigan Press.
  15. Goldberg, D. E. (1989). Genetic Algorithms in Search,Optimization and and Machine Learning. Reading, MA: Addison-Wesley.
  16. Buckles Bill P. and Petry, Frederick E. Genetic Algorithms. Los Alamitos, CA: The IEEE Computer Society Press. 1992.
  17. Haupt, Randy L, and Haupt, Sue Ellen. (1998). Practical Genetic Algorithms. New York: John wiley & Sons
  18. L.V. Allis,H.J. van den Herik ,M.P.H. Huntjens. Go-Moku and Threat Space Search.
  19. Robert E. Marks Playing Games with Genetic Algorithms
  20. Sanjay M Shah, Chirag S Thaker, Dharm Singh Multimedia Based Fitness Function OptimizationThrough Evolutionary Game Learning at International Conference on ETNCC 2011 at MPUAT, Udaipur on 22-24 April 2011.