A search for scalable evolutionary solutions to the game of MasterMind

MasterMind is a puzzle in which a hidden string of symbols must be discovered by producing query strings which are compared with the hidden one; the result of this comparison (in terms of number of correct positions and colors) is fed back to the player that is trying to crack the code (codebreaker). Methods for solving this puzzle are usually compared in terms of the number of query strings (guesses) made and the total time needed to produce those strings. In this paper we focus on the latter by trying to find a combination of parameters that is, first, uniform and independent of the problem size, and second, adequate to find a fast solution that is, at the same time, good enough. The key to this combination of parameters will be the consistent set size, that is, the maximum number of combinations that are sought before being scored and played as a guess. Having found in previous papers that the consistent set size has an influence on speed, we will concentrate on small sizes and test them through two different scoring methods from literature: most parts and entropy to find out the influence of that parameter on the outcome and which method scales better. With this we try to find out which method and size yield the best results an are effectively able to? find solutions for sizes not approached so far in a reasonable time.