Tuning Chess Evaluation Function Parameters using Differential Evolution Algorithm

This article is an extended abstract of a doctoral dissertation on chess evaluation function tuning with differential evolution (DE) algorithm. DE is adopted for efficient chess evaluation function tuning, extended with an opposition-based optimization and a new history mechanism. Experimental results show that the algorithm is efficient and can be applied to the chess evaluation function tuning that has more or less knowledge.