Finding better solutions to the mastermind puzzle using evolutionary algorithms

Abstract

The art of solving the Mastermind puzzle was initiated by Donald Knuth and is already more than thirty years old; despite that, it still receives much attention in operational research and computer games journals, not to mention the nature-inspired stochastic algorithm literature. In this paper we revisit the application of evolutionary algorithms to solving it and trying some recently-found results to an evolutionary algorithm. The most parts heuristic is used to select guesses found by the evolutionary algorithms in an attempt to find solutions that are closer to those found by exhaustive search algorithms, but at the same time, possibly have better scaling properties when the size of the puzzle increases. © 2010 Springer-Verlag Berlin Heidelberg.