Instead of playing against each other, the genetic chess engines read a PGN file of a large number of games played by professional human players or high-level engines. As the engines read the file, they step through the games while trying to predict the next move. The engines that get the most moves correct get to create the next generation.
Or, the engine plays moves of the winning side and the opponent plays the moves of the losing player, resetting the board on each move.
Since there's an upper limit to fitness (100% correct predictions), this may only be useful as an initial bootstrap to a normally run gene pool.
Instead of playing against each other, the genetic chess engines read a PGN file of a large number of games played by professional human players or high-level engines. As the engines read the file, they step through the games while trying to predict the next move. The engines that get the most moves correct get to create the next generation.
Or, the engine plays moves of the winning side and the opponent plays the moves of the losing player, resetting the board on each move.
Since there's an upper limit to fitness (100% correct predictions), this may only be useful as an initial bootstrap to a normally run gene pool.