An adaptive evolutionary algorithm based on tactical and positional chess problems to adjust the weights of a chess engine

This paper employs an evolutionary algorithm to adjust the weights of the evaluation function of a chess engine. The selection mechanism of this algorithm chooses the virtual players (individuals in the population) that have the highest number of problems properly solved from a database of tactical and positional chess problems. This method has as its main advantage that we only mutate those weights involved in the solution of the current problem. Furthermore, the mutation mechanism is based on a Gaussian distribution whose standard deviation is adapted through the number of problems solved by each virtual player. We show here how, with the use of this method, we were able to increase the rating of our chess engine in 557 Elo points (from 1760 to 2317).

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