Evolutionary computation in artificial board game playing through genetic weight evolution

Artificial Intelligence researchers have witnessed substantial implementation of evolutionary computation in test-bed application domain like board games to evolve game playing programs. A genetic algorithm is a methodology to "instill" and “tune” deterministic board game playing computer program. Evolutionary computation aims to solve problems that have very high search complexity and critical decision complexity. Game playing programs aims to play better by exploiting various possible moves by associating them with their “goodness” based on their weight values. These weights are given to specific disc positions squares according to board game feature based positional prominence. The weights are genetically evolved to arrive at move making decision. This paper focuses the application of disc set weight evolving through genetic operators induced for the Game of Othello.