  The art of solving the Mastermind puzzle was initiated by Donald Knuth and is
already more than 30 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 try to
suggest a strategy that will allow nature-inspired algorithms to obtain results
as good as those based on exhaustive search strategies; in order to do that, we
first review, compare and improve current approaches to solving the puzzle;
then we test one of these strategies with an estimation of distribution
algorithm. Finally, we try to find a strategy that falls short of being
exhaustive, and is then amenable for inclusion in nature inspired algorithms
(such as evolutionary or particle swarm algorithms). This paper proves that by
the incorporation of local entropy into the fitness function of the
evolutionary algorithm it becomes a better player than a random one, and gives
a rule of thumb on how to incorporate the best heuristic strategies to
evolutionary algorithms without incurring in an excessive computational cost.
