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Beating exhaustive search at its own game: revisiting evolutionary mastermind

Published: 07 July 2010 Publication History

Abstract

The Mastermind puzzle consists in finding out a secret combination by playing others in the same search space and using the hints obtained as a response (which reveal how close the played combination is to the secret one) to produce new combinations and eventually the secret one. Despite having been researched for a number of years, there are still several open issues, such as finding a strategy to select the next combination to play that is able to consistently obtain good results, at any problem size, and also doing it in as little time as possible. In this paper we cast this as a constrained optimization problem, introducing a new fitness function for evolutionary algorithms that takes that fact into account, and compare it to other solutions (exhaustive/heuristic and evolutionary), finding that it is able to obtain the consistently good solutions, and in as little as 30% less time than previously published evolutionary algorithms [2].

References

[1]
Juan-Julián Merelo and Thomas P. Runarsson. Finding better solutions to the mastermind puzzle using evolutionary algorithms. volume 6024 of Lecture Notes in Computer Science, pages 120--129, Istanbul, Turkey, 7 - 9 April 2010. Springer-Verlag. EvoApplications2010 to be held in conjunction with EuroGP-2010, EvoCOP2010 and EvoBIO2010. To appear.
[2]
Eric W. Weisstein. Mastermind. From MathWorld-A Wolfram Web Resource.
[3]
B. Kooi. Yet another Mastermind strategy. ICGA Journal,28(1):13--20, 2005.
[4]
L. Berghman, D. Goossens, and R. Leus. Efficient solutions for Mastermind using genetic algorithms. Computers and Operations Research, 36(6):1880--1885, 2009.
[5]
T. P Runarsson and J. J. Merelo. Adapting heuristic Mastermind strategies to evolutionary algorithms. In NICSO'10 Proceedings, LNCS. Springer-Verlag, 2010. To be published, also available from ArXiV: http://arxiv.org/abs/0912.2415v1.

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Published In

cover image ACM Conferences
GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation
July 2010
1520 pages
ISBN:9781450300728
DOI:10.1145/1830483

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 July 2010

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Author Tags

  1. evolutionary algorithms
  2. games
  3. heuristics
  4. hybrid algorithms

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