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Solving Mastermind Using Genetic Algorithms
Tom Kalisker and Doug Camens
Software Engineering Masters Program
Engineering and Information Science Division
Penn State Great Valley School of Graduate Professional Studies
30 East Swedesford Road
Malvern, PA 19355-1443
{trk139,dmc123}@psu.edu
Abstract.
The MasterMind game involves decoding a secret code. The classic
game is a code of six possible colors in four slots. The game has
been analyzed and optimal strategies have been posed by computer
scientists and mathematicians. In this paper we will survey
previous work done on solving MasterMind, including several
approaches using Genetic Algorithms. We will also analyze the
solution sets and compare our results using a novel scoring system
inside a GA against previous work using Genetic and Heuristic
algorithms. Our GA is performing closer to optimal then previously
published work. The GA we present is a Steady State GA using
Fitness Proportional Reproduction (FPR), where the fitness
function incorporates a simple heuristic algorithm. We also
present a scoring method that is simpler then those used by other
researchers. In larger games such as 10 colors and 8 slots our GA
clearly outperform the heuristic algorithm. In fact if one wishes
to tradeoff a higher average number of guesses to a faster running
time, extremely large games such as 
can be solved in a
reasonable time (i.e. minutes) of run time.
LNCS 2724, p. 1590 ff.
lncs@springer.de
© Springer-Verlag Berlin Heidelberg 2003