Optimizing Genetic Algorithm Parameters for a Stochastic Game.

Can't Stop is a jeopardy stochastic game played on an octagonal game board with four six-sided dice. Previous work generalized a well-known heuristic strategy for the solitaire game and attempted to optimize the parameters of the generalized strategy using a genetic algorithm (GA). There were two challenges in that optimization process: first, the stochastic nature of the game results in a very noisy fitness function; second, the fitness function is computationally expensive. In this work we continue the optimization process for the heuristic strategy by optimizing the GA: for a fixed number of fitness function evaluations, we investigate the effects of varying the GA parameters (in particular the population size and number of generations), which in turn affect the number of samples per individual and thus noise as well. We also examine different sampling schedules; our schedules are unique in that selecting the final champion is considered a schedulable phase. The GA parameters are first optimized on an easy-to-compute test function. The resulting GA parameters are effective on the original problem and as a result we obtain an improved heuristic strategy for Can't Stop.