Simulating human grandmasters
Omid David-Tabibi, H.J. van den Herik, Moshe Koppel, Nathan S. Netanyahu · 2009
This paper demonstrates the use of genetic algorithms for evolving a grandmaster-level evaluation function for a chess program. This is achieved by combining supervised and unsupervised learning. In the supervised learning phase the organisms are evolved to mimic the behavior of human grandmasters, and in the unsupervised learning phase these evolved organisms are further improved upon by means of coevolution.