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New solver and optimal anticipation strategies design based on evolutionary computation for the game of MasterMind

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Abstract

This paper presents and compares several evolutionary solutions for the well-known MasterMind game, a classic board game invented in the 1970s. First, we propose a novel evolutionary approach (which we call nested hierarchical evolutionary search) to solve the MasterMind game, comparing the obtained results with that of existing algorithms. Second, we show how to design novel game anticipation strategies for the MasterMind game, by applying genetic programming. In this case we compare the performance of the new obtained strategies with that of the classical ones, obtaining advantages in all the cases tested.

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Notes

  1. We do not consider WC since it is focussed on minimizing the maximum depth of the game tree, instead of minimizing the tree weight. ES, MP and EN are focussed on minimizing the tree weight, and are known to be more efficient than WC.

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Acknowledgments

This work has been partially supported by Spanish Ministry of Science and Innovation, under project numbers ECO2010-22065-C03-02, TIN2011-28627-C04-02 and P08-TIC-03903 awarded by the Andalusian Regional Government, as well as project CANUBE (CEI2013-P-14) from CEI-BioTIC (http://biotic.ugr.es).

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Authors and Affiliations

  1. Department of Signal Theory and Communications, Escuela Politécnica Superior, Universidad de Alcalá, 28871 Alcalá de Henares, Madrid, Spain

    J. Maestro-Montojo & S. Salcedo-Sanz

  2. Departamento de Arquitectura y Tecnología de Computadores, Universidad de Granada, Granada, Spain

    J. J. Merelo

Authors
  1. J. Maestro-Montojo
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  2. S. Salcedo-Sanz
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  3. J. J. Merelo
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Correspondence to S. Salcedo-Sanz.

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Maestro-Montojo, J., Salcedo-Sanz, S. & Merelo, J.J. New solver and optimal anticipation strategies design based on evolutionary computation for the game of MasterMind. Evol. Intel. 6, 219–228 (2014). https://doi.org/10.1007/s12065-013-0099-6

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