  This paper studies the optimization of strategies in the context of possibly
randomized two players zero-sum games with incomplete information. We compare 5
algorithms for tuning the parameters of strategies over a benchmark of 12
games. A first evolutionary approach consists in designing a highly randomized
opponent (called naive opponent) and optimizing the parametric strategy against
it; a second one is optimizing iteratively the strategy, i.e. constructing a
sequence of strategies starting from the naive one. 2 versions of coevolutions,
real and approximate, are also tested as well as a seed method. The coevolution
methods were performing well, but results were not stable from one game to
another. In spite of its simplicity, the seed method, which can be seen as an
extremal version of coevolution, works even when nothing else works.
Incidentally, these methods brought out some unexpected strategies for some
games, such as Batawaf or the game of War, which seem, at first view, purely
random games without any structured actions possible for the players or Guess
Who, where a dichotomy between the characters seems to be the most reasonable
strategy. All source codes of games are written in Matlab/Octave and are freely
available for download.
