Evolutionary swarm neural network game engine for Capture Go.

Evaluation of the current board position is critical in computer game engines. In sufficiently complex games, such a task is too difficult for a traditional brute force search to accomplish, even when combined with expert knowledge bases. This motivates the investigation of alternatives. This paper investigates the combination of neural networks, particle swarm optimization (PSO), and evolutionary algorithms (EAs) to train a board evaluator from zero knowledge. By enhancing the survivors of an EA with PSO, the hybrid algorithm successfully trains the high-dimensional neural networks to provide an evaluation of the game board through self-play. Experimental results, on the benchmark game of Capture Go, demonstrate that the hybrid algorithm can be more powerful than its individual parts, with the system playing against EA and PSO trained game engines. Also, the winning results of tournaments against a Hill-Climbing trained game engine confirm that the improvement comes from the hybrid algorithm itself. The hybrid game engine is also demonstrated against a hand-coded defensive player and a web player.