The soft computing approach for gaming is different from the traditional one that exploits knowledge of the opening, middle, and endgame stages. It is aims to evolve efficiently some simple heuristics that can be created easily from the basic knowledge of the game. Integrating sphere knowledge into soft computation can enhance the performance of evolved algorithmic methodologies and quicken the learning of solution finding. In this paper, one of the major constituents of soft computing- genetic algorithm approach is employed to develop a game playing program for Reversi (Game of Othello). Evaluation function based genetic game playing strategies are been used to implement than a single simple heuristic based one. Genetic parameters implemented using Reversi game based fitness function using min –max search algorithm is strategic combination focus of the paper. Experimental results show that the proposed method is promising for generating better strategies.Developing players programs for board games has been part of novel soft computing research arms for decades. Board games have precise, easily formalized rules that make them perfect modeling in a programming environment. In this paper focus is on full knowledge (perfect information), deterministic, zero-sum board games by inculcating genetic algorithm as better move making search optimization.

1. S. Chong, D. Ku, H. Lim, M. Tan, and J. White. Evolved neural networks learning othello strategies. In Evolutionary Computation, 2003. CEC ’03. The 2003 Congress on, volume 3, pages 2222 – 2229 Vol.3, December 2003.
2. Daugman J. G., “How iris recognition works”. Proceedings of 2002 International Conference on Image Processing, Vol. 1,2002.
3. 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.
4. 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.
5. M. Hlynka and J. Schaeffer. Automatic generation of search engines. In Advances in Computer Games, pages 23–38, 2006.
6. Rosenbloom, P. (1982). A world championship level Othello program. Artificial Intelligence, 19:279-320.
7. J. R. Koza. Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA, USA, 1992.
8. Holland, J. H. Adaptation in Natural and Artificial Systems. University of Michigan Press, 1975
9. 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.
10. S. Luke. Code growth is not caused by introns. In D. Whitley, editor, Late Breaking Papers at the 2000 Genetic and Evolutionary Computation Conference, pages 228–235, Las Vegas, Nevada, USA, July 2000.
11. T. P. Runarsson and S. M. Lucas. Coevolution versus self-play temporal difference learning for acquiring position evaluation in small-board Go. IEEE Transactions on Evolutionary Computation, 9(6):628–640, 2005.
12. J. Schaeffer, N. Burch, Y. Bjornsson, A. Kishimoto, M. Muller, R. Lake, P. Lu, and S. Sutphen. Checkers is solved. Science, 317(5844):1518–1522, 2007.
13. Y. Jin and J. Branke. Evolutionary optimization in uncertain environments – a survey. IEEE Trans. Evolutionary Computation, 9(3):303–317, 2005.
14. Y. Jin and B. Sendhoff. Tradeoff between performance and robustness: An evolutionary multiobjective approach. In Proc. Evolutionary Multi-Criterion Optimization, LNCS 2632, pages 237–251, 2003.
15. Kargupta, H., and B. H. Park. Gene expression and fast construction of distributed evolutionary representation, Evolutionary Computation, pp 43-69, 2001.
16. Linton, R.C. Policies for Managing Composite, Epistatic Fitness Functions in Genetic Algorithms,” Proceedings of the 40th Annual ACM Southeast Conference, pp 23-30, ACM Press, 2002.
17. McClure, M., and R. C. Linton, Proper Timing of Fitness Function Adaptation in Genetic Algorithms, Proceedings of the 41st Annual Southeast ACM Conference, pp 251-256, ACM Press, 2003.
18. Mitchell, M. An Introduction to Genetic Algorithms, Cambridge, MA:MIT Press, 1996.
19. M. Muller, “Computer Go,” Artificial Intelligence, vol. 134, pp. 145–179, 2002.
20. Naudts, B., Suys D., and Verschoren A. Epistasis as a basic concept in formal landscape analysis. Proceedings of the 7th International Conference on Genetic Algorithms, 1997.
21. Lee, K. -F., and Mahajan, S. (1990). The development of a world class Othello program. Artificial Intelligence, 43:21-36.
22. Matt Gilgenbach. Fun game AI design for beginners. In Steve Rabin, editor, AI Game Programming Wisdom 3, 2006.
23. Singh Dharm, Thaker Chirag S and Shah Sanjay M. Fitness Value Optimization for Disc Set in Board Game Through Evolutionary Learning in IJCA 2011:3728/encc/017 IJCA Special Issue on “Evolution in Networks and Computer Commumnication” ISBN 978-93-80864-98-7.
24. 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.
25. 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.

Soft Computing Reversing Fitness Function Genetic Algorithm Genetic Weight