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Using Genetic Algorithm to Solve Game of Go-Moku

Author(s): Sanjay M Shah | Dharm Singh | J.S Shah

Journal: International Journal of Computer Applications
ISSN 0975-8887

Volume: ooc;
Issue: 1;
Date: 2012;
Original page

Keywords: Population | Chromosome | Fitness Function | Genetic operators.

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.
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