Abstract
In this chapter, we use the problem of solving Sudoku puzzles to demonstrate the possibility of achieving practical processing time through the use of many-core processors for parallel processing in the application of evolutionary computation. To increase accuracy, we propose a genetic operation that takes building-block linkage into account. As a parallel processing model for higher performance, we use a multiple-population coarse-grained genetic algorithm (GA) model to counter initial value dependence under the condition of a limited number of individuals. The genetic manipulation is also accelerated by the parallel processing of threads. In an evaluation using even very difficult problems, we show that execution times of several tens of seconds and several seconds can be obtained by parallel processing with the Intel Core i7 and NVIDIA GTX 460, respectively, and that a correct solution rate of 100 % can be achieved in either case. In addition, genetic operations that take linkage into account are suited to fine-grained parallelization and thus may result in an even higher performance.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Gordon, V.S., Whitley, D.: Serial and parallel genetic algorithms as function optimizers. In: Proceedings of the 5th International Conference on Genetic Algorithms, pp. 177–183. Morgan Kaufmann Publishers Inc., San Franciso, CA USA (1993)
Mühlenbein, H.: Parallel genetic algorithms, population genetics and combinatorial optimization. In: Proceedings of the 3rd International Conference on Genetic Algorithms, pp. 416–421 (1989)
Mühlenbein, H.: Evolution in time and space - the parallel genetic algorithm. In: Foundations of Genetic Algorithms, pp. 316–337. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1991)
Shonkwiler, R.: Parallel genetic algorithm. In: Proceedings of the 5th International Conference on Genetic Algorithms, pp. 199–205 (1993)
Cantu-Paz, E.: Efficient and Accurate Parallel Genetic Algorithms. Kluwer Academic Publishers, Norwell, MA,USA (2000)
Byun, J.H., Datta, K., Ravindran, A., Mukherjee, A., Joshi, B.: Performance analysis of coarse-grained parallel genetic algorithms on the multi-core sun UltraSPARC T1. In: SOUTHEASTCON’09, pp. 301–306. IEEE, Danvers, MA, USA (2009)
Serrano, R., Tapia, J., Montiel, O., Sepúlveda, R., Melin, P.: High performance parallel programming of a GA using multi-core technology. In: Castillo, O., Melin, P., Kacprzyk, J., Pedrycz, W., eds.: Soft Computing for Hybrid Intelligent Systems. Volume 154 of Studies in Computational Intelligence, pp. 307–314. Springer, Berlin/Heidelberg (2008)
Tsutsui, S., Fujimoto, N.: Solving quadratic assignment problems by genetic algorithms with GPU computation: a case study. In: GECCO ’09: Proc. 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference, pp. 2523–2530 (2009)
Munawar, A., Wahib, M., Munetomo, M., Akama, K.: Theoretical and empirical analysis of a GPU based parallel Bayesian optimization algorithm. In: Proceedings of the 2009 International Conference on Parallel and Distributed Computing, Applications and Technologies. PDCAT ’09, pp. 457–462 (2009)
Sato, M., Sato, Y., Namiki, M.: Proposal of a multi-core processor from the viewpoint of evolutionary computation. In: Proceedings of the IEEE Congress on Evolutionary Computation 2010, pp. 3868–3875 (July 2010)
Wikipedia: Sudoku. Available via WWW: http://en.wikipedia.org/wiki/Sudoku (cited 8.3.2010)
Wikipedia: Backtracking. Available via WWW: http://en.wikipedia.org/wiki/Backtracking (cited 1.11.2011)
IEEE: ISO/IEC 9945-1 ANSI/IEEE Std 1003.1. (1996)
Sato, Y., Inoue, H.: Solving Sudoku with genetic operations that preserve building blocks. In: Proceedings of the IEEE Conference on Computational Intelligence in Game, pp. 23–29 (2010)
Lewis, R.: Metaheuristics can solve Sudoku puzzles. J. Heuristics 13 387–401 (2007)
Simonis, H.: Sudoku as a constraint problem. In: Proc. of the 4th Int. Workshop Modelling and Reformulating Constraint Satisfaction Problems International Conference on Genetic Algorithms, pp. 13–27 (2005)
Lynce, I., Ouaknine, J.: Sudoku as a SAT problem. In: Proceedings of the 9 th International Symposium on Artificial Intelligence and Mathematics, AIMATH 2006 (2006)
Moon, T., Gunther, J.: Multiple constraint satisfaction by belief propagation: An example using Sudoku. In: Proceedings of the 2006 IEEE Mountain Workshop on Adaptive and Learning Systems (SMCals 2006), pp. 122–126. IEEE, Los Alamitos, CA, USA (2006)
Mantere, T., Koljonen, J.: Solving and ranking Sudoku puzzles with genetic algorithms. In: Proceedings of the 12th Finnish Artificial Conference STeP 2006, pp. 86–92 (October 2006)
Mantere, T., Koljnen, J.: Solving, rating and generating Sudoku puzzles with GA. In: Proceedings of the IEEE Congress on Evolutionary Computation 2007, pp. 1382–1389 (July 2007)
Moraglio, A., Togelius, J., Lucas, S.: Product geometric crossover for the Sudoku puzzle. In: Proceedings of IEEE Congress on Evolutionary Computation 2006, pp. 470–476 (July 2006)
Galvan-Lopez, E., O’Neill, M.: On the effects of locality in a permutation problem: The Sudoku puzzle. In: Proceedings of IEEE Symposium on Computational Intelligence and Games (CIG 2009), pp. 80–87 (September 2009)
Goldberg, D.E., Sastry, K.: A practical schema theorem for genetic algorithm design and tuning. In: Proceedings of the 2001 Genetic and Evolutionary Computation Conference, pp. 328–335 (2001)
Super Difficult Sudoku’s. Available via WWW: http://lipas.uwasa.fi/~timan/sudoku/EA_ht_2008.\pdf#search=’CT20A6300%20Alternative%20Project%20work%202008’ (cited 8.3.2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Sato, Y., Hasegawa, N., Sato, M. (2013). Acceleration of Genetic Algorithms for Sudoku Solution on Many-Core Processors. In: Tsutsui, S., Collet, P. (eds) Massively Parallel Evolutionary Computation on GPGPUs. Natural Computing Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37959-8_19
Download citation
DOI: https://doi.org/10.1007/978-3-642-37959-8_19
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37958-1
Online ISBN: 978-3-642-37959-8
eBook Packages: Computer ScienceComputer Science (R0)