Genetic Algorithms for Evolving Computer Chess Programs
This work addresses the challenge of creating competitive AI for chess, which is incremental as it applies known genetic algorithms to a specific domain.
The paper tackles the problem of developing high-performance computer chess programs by using genetic algorithms to evolve both evaluation functions and search mechanisms, resulting in a program that outperforms a two-time world computer chess champion and matches other leading programs.
This paper demonstrates the use of genetic algorithms for evolving: 1) a grandmaster-level evaluation function, and 2) a search mechanism for a chess program, the parameter values of which are initialized randomly. The evaluation function of the program is evolved by learning from databases of (human) grandmaster games. At first, the organisms are evolved to mimic the behavior of human grandmasters, and then these organisms are further improved upon by means of coevolution. The search mechanism is evolved by learning from tactical test suites. Our results show that the evolved program outperforms a two-time world computer chess champion and is at par with the other leading computer chess programs.