Not necessarily. If there are factors of a board position that make it strong regardless of future moves, then why can't that information be encoded in the fitness function without having to play future moves forward?
Sure, but how would you know? Sacrificing officers in chess is a typical example. Negative expected value short term, but typically long term positive when done intentionally. But generally, if you can come up with a robust fitness function, genetic algorithm is one method, although typically there are better alternatives giving faster results. In my experience, genetic algorithms can be a great start to get something but typically slow. Which matters if you need to simulate a few billion games... Interesting article anyway, thanks.
