Optimizing worst-case scenario in evolutionary solutions to the MasterMind puzzle

The MasterMind puzzle is an interesting problem to be approached via evolutionary algorithms, since it is at the same time a constrained and a dynamic problem, and has eventually a single solution. In previous papers we have presented and evaluated different evolutionary algorithms to this game and shown how their behavior scales with size, looking mainly at the game-playing performance. In this paper we fine-tune the parameters of the evolutionary algorithms so that the worst case number of evaluations, and thus the average and median, are improved, resulting in a better solution in a more reliably predictable time.