Abstract
In this work we improve previous approaches based on genetic algorithms (GA) to solve sudoku puzzles. Those approaches use random swap mutations and filtered mutations, where both operations result in relatively slow convergence, the latter suffering a bit less. We suggest to improve GA based approaches by an intermediate local optimization step of the population. Compared to the previous approaches our approach is superior in terms of convergence rate, success rate and speed. As consequence we find the optimum with one population member and within one generation in a few milliseconds instead of nearly one minute.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Barras C (2010) What a maze-solving oil drop can tell us about intelligence. New Sci 205(2744):8–9
Bartlett A, Chartier TP, Langville AN, Rankin TD (2008) An integer programming model for the Sudoku problem. J Online Math Appl 8 (2008)
Becker M (2006) Genetic algorithms for noise reduction in tire design. In: 2006 IEEE international conference on systems, man and cybernetics, vol 6, pp 5304–5308, Oct 2006. https://doi.org/10.1109/ICSMC.2006.385151
Felgenhauer B, Jarvis F (2005) Enumerating possible Sudoku grids. http://www.afjarvis.staff.shef.ac.uk/sudoku/sudoku.pdf
Inkala A (2007) AI Escargot—the most difficult sudoku puzzle. Lulu Publisher, Finland
Lynce I, Ouaknine J (2006) Sudoku as a sat problem. In: ISAIM
Manter T, Koljonen J (2007) Solving, rating and generating Sudoku puzzles with GA. In: IEEE congress on evolutionary computation, 2007. CEC 2007. IEEE, pp 1382–1389 (2007)
Moraglio A, Togelius J, Lucas S (2006) Product geometric crossover for the Sudoku puzzle. In: IEEE congress on evolutionary computation, 2006. CEC 2006. IEEE, pp 470–476 (2006)
Sato Y, Inoue H (2010) Solving Sudoku with genetic operations that preserve building blocks. In: 2010 IEEE symposium on computational intelligence and games (CIG). IEEE, pp 23–29 (2010)
Sörensen K (2013) Metaheuristics—the metaphor exposed. Int Trans Oper Res (2013)
Syrjakow M, Szczerbicka H (1999) Efficient parameter optimization based on combination of direct global and local search methods. In: Evolutionary algorithms. Springer, pp 227–249
Syrjakow M, Szczerbicka H, Becker M (1998) Genetic algorithms: a tool for modelling, simulation, and optimization of complex systems. Cybern Syst 29(7):639–659
Takayuki Y, Takahiro S (2003) Complexity and completeness of finding another solution and its application to puzzles. IEICE Trans Fundam Electron Commun Comput Sci 86(5):1052–1060
Wang Z, Yasuda T, Ohkura K (2015) An evolutionary approach to Sudoku puzzles with filtered mutations. In: 2015 IEEE congress on evolutionary computation (CEC). IEEE, pp 1732–1737 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Becker, M., Balci, S. (2019). Improving an Evolutionary Approach to Sudoku Puzzles by Intermediate Optimization of the Population. In: Kim, K., Baek, N. (eds) Information Science and Applications 2018. ICISA 2018. Lecture Notes in Electrical Engineering, vol 514. Springer, Singapore. https://doi.org/10.1007/978-981-13-1056-0_38
Download citation
DOI: https://doi.org/10.1007/978-981-13-1056-0_38
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-1055-3
Online ISBN: 978-981-13-1056-0
eBook Packages: EngineeringEngineering (R0)