research-article

Improving evolutionary solutions to the game of mastermind using an entropy-based scoring method

Authors:

Juan Julián Merelo-Guervós,

Pedro Castillo,

Antonio M. Mora García,

Anna I. Esparcia-AlcázarAuthors Info & Claims

GECCO '13: Proceedings of the 15th annual conference on Genetic and evolutionary computation

Pages 829 - 836

https://doi.org/10.1145/2463372.2463473

Published: 06 July 2013 Publication History

Abstract

Solving the MasterMind puzzle, that is, finding out a hidden combination by using hints that tell you how close some strings are to that one is a combinatorial optimization problem that becomes increasingly difficult with string size and the number of symbols used in it. Since it does not have an exact solution, heuristic methods have been traditionally used to solve it; these methods scored each combination using a heuristic function that depends on comparing all possible solutions with each other. In this paper we first optimize the implementation of previous evolutionary methods used for the game of mastermind, obtaining up to a 40% speed improvement over them. Then we study the behavior of an entropy-based score, which has previously been used but not checked exhaustively and compared with previous solutions. The combination of these two strategies obtain solutions to the game of Mastermind that are competitive, and in some cases beat, the best solutions obtained so far. All data and programs have also been published under an open source license.

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Cited By

Taylor NHofer MNelson J(2020)The Paradox of Help Seeking in the Entropy Mastermind GameFrontiers in Education10.3389/feduc.2020.5339985Online publication date: 23-Sep-2020
https://doi.org/10.3389/feduc.2020.533998
Fiore TLang APerucca A(2018)Tactile Tools for Teaching: Implementing Knuth's Algorithm for Mastering MastermindThe College Mathematics Journal10.1080/07468342.2018.150198949:4(278-286)Online publication date: 6-Sep-2018
https://doi.org/10.1080/07468342.2018.1501989

Index Terms

Improving evolutionary solutions to the game of mastermind using an entropy-based scoring method
1. Mathematics of computing
  1. Mathematical analysis
    1. Mathematical optimization
2. Theory of computation
  1. Design and analysis of algorithms
    1. Mathematical optimization

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cover image ACM Conferences

GECCO '13: Proceedings of the 15th annual conference on Genetic and evolutionary computation

July 2013

1672 pages

ISBN:9781450319638

DOI:10.1145/2463372

Editor:
Christian Blum
IKERBASQUE and University of the Basque Country UPV/EHU, Spain
,
General Chair:
Enrique Alba
University of Malaga, Spain

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Published: 06 July 2013

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GECCO '13: Genetic and Evolutionary Computation Conference

July 6 - 10, 2013

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GECCO '13 Paper Acceptance Rate 204 of 570 submissions, 36%;

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Taylor NHofer MNelson J(2020)The Paradox of Help Seeking in the Entropy Mastermind GameFrontiers in Education10.3389/feduc.2020.5339985Online publication date: 23-Sep-2020
https://doi.org/10.3389/feduc.2020.533998
Fiore TLang APerucca A(2018)Tactile Tools for Teaching: Implementing Knuth's Algorithm for Mastering MastermindThe College Mathematics Journal10.1080/07468342.2018.150198949:4(278-286)Online publication date: 6-Sep-2018
https://doi.org/10.1080/07468342.2018.1501989

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