Abstract
In this paper we propose a novel evolutionary approach to solve the Mastermind game, and compare the results obtained with that of existing algorithms. The new evolutionary approach consists of a hierarchical one involving two different evolutionary algorithms, one for searching the set of eligible codes, and the second one to choose the best code to be played at a given stage of the game. The comparison with existing algorithms provides interesting conclusions regarding the performance of the algorithms and how to improve it in the future. However, it is clear that Entropy is a better scoring strategy than Most Parts, at least for these sizes, being able to obtain better results, independently of the evolutionary algorithm.
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Maestro-Montojo, J., Merelo, J.J., Salcedo-Sanz, S. (2013). Comparing Evolutionary Algorithms to Solve the Game of MasterMind. In: Esparcia-Alcázar, A.I. (eds) Applications of Evolutionary Computation. EvoApplications 2013. Lecture Notes in Computer Science, vol 7835. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37192-9_31
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DOI: https://doi.org/10.1007/978-3-642-37192-9_31
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