Using Memetic Algorithms to Optimize Run-Timein Genetic Playing of Mastermind

Mastermind is an interesting dynamic constraint satisfaction problem which resembledcracking or code breaking. Therefore, solving Mastermind especially applying geneticalgorithms in it has received much attention in the literature. Genetic algorithms are able tobreak the code in a low number of guesses, however, they suffer from long run-times. Toaddress this problem, in this paper, we presented memetic algorithms to solve Mastermind.Specifically, we applied simulated annealing in the different generations of the geneticalgorithm to locate local minimums more efficiently. Our results showed that not only thememetic algorithm solved Mastermind in a shorter time than the genetic algorithm but alsoslightly fewer guesses were required.

Using Memetic Algorithms to Optimize Run-Timein Genetic Playing of Mastermind Keywords:

Mastermind , Memeticalgorithms , Genetic algorithm , Simulated annealing algorithm , Game.