Genetic algorithm is a stochastic parallel beam search that can be applied to many typical search problems. This paper describes a genetic algorithmic approach to a problem in artificial intelligence. During the process of evolution, the environment cooperates with the population by continuously making itself friendlier so as to lower the evolutionary pressure. Evaluations show the performance of this approach seems considerably effective in solving this type of board games. Game-playing programs are often described as being a combination of search and knowledge. Board Games provide dynamic environments that make them ideal area of computational intelligence theories, architectures, and algorithms. Evolutionary algorithms such as Genetic algorithm are applied to the game playing because of the very large state space of the problem. 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. Matt Gilgenbach. Fun game AI design for beginners. In Steve Rabin, editor, AI Game Programming Wisdom 3, 2006.
4. 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.
5. 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.
6. L. Victor Allis, “Searching for solutions in Games and Artificial Intelligence”, Ph D Thesis
7. Marco Kunze and Sebastian Nowozin in their study “An AI for Gomoku/Wuziqi ? and more...”
8. Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization and and Machine Learning. Reading, MA: Addison-Wesley.
9. Ting Qian “Using Genetic Algorithm to Solve Sliding Tile Puzzles”.
10. Sanjay M Shah, Dharm Singh, Chirag S Thaker “Optimization of Fitness Function through Evolutionary Game Learning”, Evolution in Networks and Computer Communications-2011, A Special Issue from IJCA.

Population Chromosome Fitness Function Genetic operators.