Abstract
In this paper we present a new version of an
evolutionary algorithm that finds the hidden
combination in the game of MasterMind by using hints on
how close is a combination played to it. The
evolutionary algorithm finds the hidden combination in
an optimal number of guesses, is efficient in terms of
memory and CPU, and examines only a minimal part of the
search space. The algorithm is fast, and indeed
previous versions can be played in real time on the
world wide web. This new version of the algorithm is
presented and compared with theoretical bounds and
other algorithms. We also examine how the algorithm
scales with search space size, and its performance for
different values of the EA parameters.
Users
Please
log in to take part in the discussion (add own reviews or comments).