The first paper is literally a statement of problems, whereas the second reports on using GAs to tune the computationally expensive answer to the problem, i.e. picking values for using in the heuristics.
The results of using the GA were actually quite reassuring. Improvements of 8-20% over the hand-tuned solution are reported.
This is an interesting paper about the challenges of finding a good evaluation function for genetic algorithms. They use GAs to evolve a two populations, tested against each other. Interestingly there is this quote:
For Nim, a perfect solution can be found after about 150 million games with parameters tuned well. Typically, success in the competition for Nim is much more balanced between the two population during most of the tun. The first population spends much time trying strategies which employ an incorrect first move. Such strategies can never be perfect, but can be difficult to beat if most other moves had been optimised. Eventually, a strategy employing a correct first move is optimized to the point of perfection.
Evolving a player for go could avoid this problem by using randomised starting points for a significant number of games. This would encourage the evolution of strategies that are truly good in the middle and end game, irrespective of their performance in the opening, thus splitting the problem in two. Maybe random games from an archive could be used as starting positions for solving the end game, splitting it into three?
