Quality of state improvisation through evaluation function optimization in genetic application learning

Artificial intelligence algorithms have been applied on computer board games since 1950s. Board Games provide competitive, cognitive learning and dynamic environments that make them ideal area of computational intelligence theories, architectures, and algorithms. Natural Evolution can also be considered to be a game play in which the rewards for a test organism that plays a good game of life are the propagation of its genetic material to its successors and its continued survival. 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, because of its low branching property, Reversi (Game of Othello) playing program can easily defeat humans by designing with strategies based game moves. Here on, the goal of computer Reversi game is no longer to challenge or defeat human players but to compete and evolve against other computer programs. This paper mainly highlights optimization of Reversi program fitness values by applying genetic operators through linear evaluation function.