Explanation-based learning: a survey of programs and perspectives
ACM Computing Surveys (CSUR)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Simulating human grandmasters: evolution and coevolution of evaluation functions
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Evolution of an efficient search algorithm for the mate-in-N problem in chess
EuroGP'07 Proceedings of the 10th European conference on Genetic programming
Expert-driven genetic algorithms for simulating evaluation functions
Genetic Programming and Evolvable Machines
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
GP-EndChess: using genetic programming to evolve chess endgame players
EuroGP'05 Proceedings of the 8th European conference on Genetic Programming
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Here, we propose an evolutionary algorithm (i.e., evolutionary programming) for tuning the weights of a chess engine. Most of the previous work in this area has normally adopted co-evolution (i.e., tournaments among virtual players) to decide which players will pass to the following generation, depending on the outcome of each game. In contrast, our proposed method uses evolution to decide which virtual players will pass to the next generation based on the number of positions solved from a number of chess grandmaster games. Using a search depth of 1-ply, our method can solve 40.78% of the positions evaluated from chess grandmaster games (this value is higher than the one reported in the previous related work). Additionally, our method is capable of solving 53.08% of the positions using a historical mechanism that keeps a record of the ''good'' virtual players found during the evolutionary process. Our proposal has also been able to increase the competition level of our search engine, when playing against the program Chessmaster (grandmaster edition). Our chess engine reached a rating of 2404 points for the best virtual player with supervised learning, and a rating of 2442 points for the best virtual player with unsupervised learning. Finally, it is also worth mentioning that our results indicate that the piece material values obtained by our approach are similar to the values known from chess theory.