Multimedia based fitness function optimization through evolutionary game learning

One of the areas of Artificial intelligence is Board Game Playing. Game-playing programs are often described as being a combination of search and knowledge. The board games are very popular. Board Games provide dynamic environments that make them ideal area of computational intelligence theories, architectures, and algorithms. Building a quality evaluation function is usually a lot of manual hard work and luck. The goodness 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. In 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 (Five-inLine), which is a variant of a Game of GO. This paper mainly highlights how genetic algorithm can be applied to game of Go-moku, where fitness values can be used by applying genetic operators through linear evaluation function.