An investigation into tournament poker strategy using evolutionary algorithms

In this paper we assess the hypothesis that a strategy including information related to game-specific factors in a poker tournament performs better than one founded on hand strength knowledge alone. Specifically, we demonstrate that the use of information pertaining to opponents’ prior actions, the stage of the tournament, one’s chip stack size and seating position all contribute towards a statistically significant improvement in the number of tournaments won. Additionally, we test the hypothesis that a strategy which combines information from all the aforementioned factors performs better than one which employs only a single factor.
We show that an evolutionary algorithm is successfully able to resolve conflicting signals from the specified factors, and that the resulting strategies are statistically stronger.