Obviously, trying to apply the min-max algorithm on the complete tree of moves works only for small games (I apologize to all chess enthusiasts, by "small" I do not mean "simplistic"). For typical turn-based strategy games where the board is often wider than 100 tiles and all pieces in a side can move simultaneously, the min-max algorithm is inapplicable.

I was wondering if a partial min-max algorithm which limits itself to N board configurations at each depth couldn't be good enough? Using a genetic algorithm, it might be possible to find a number of board configurations that are good wrt to the evaluation function. Hopefully, these configurations might also be good wrt to long-term goals.

I would be surprised if this hasn't been thought of before and tried. Has it? How does it work?