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Comparing Evolutionary Algorithms to Solve the Game of MasterMind

  • Javier Maestro-Montojo
  • Juan Julián Merelo
  • Sancho Salcedo-Sanz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7835)

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.

Keywords

Evolutionary Algorithm Evolutionary Approach Mastermind Strategy Anticipation Strategy Apply Soft Computing 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Javier Maestro-Montojo
    • 1
  • Juan Julián Merelo
    • 2
  • Sancho Salcedo-Sanz
    • 2
  1. 1.Department of Signal Processing and CommunicationsUniversidad de AlcaláMadridSpain
  2. 2.Departamento de Arquitectura y Tecnología de ComputadoresUniversidad de GranadaGranadaSpain

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