Optimizing search via diversity enhancement in evolutionary MasterMind

A MasterMind player must discover a secret combination by making guesses using the hints obtained as a response to the previous ones. Finding a general strategy that scales well with problem size is still an open issue, despite having been approached from different angles, including evolutionary algorithms. In previous papers we have tested different approaches to the evolutionary MasterMind and having found out that diversity is essential in this kind of combinatorial optimization problems, in this paper we try to tune the search methods to keep a high diversity level and thus obtain solutions to the puzzle in less average evaluations, and, if possible, in less number of combinations played. This will allow us to get improvements in the time that will be used to explore problems of bigger size.