Abstract
Cross-matching puzzles are logic based games being played with numbers, letters or symbols that present combinational problems. A cross-matching puzzle consists of three tables: solution table, detection table, and control table. The puzzle can be solved by superposing the detection and control tables. For the solution of the cross-matching puzzle, a depth first search method can be used, but by expanding the size of the puzzle, computing time can be increased. Hence, the genetic algorithm, which is one of the most common optimization algorithms, was used to solve cross-matching puzzles. The multi-layer genetic algorithm was improved for the solution of cross-matching puzzles, but the results of the multi-layer genetic algorithm were not good enough because of the expanding size of the puzzle. Therefore, in this study, the genetic algorithm was improved in an intelligent way due to the structure of the puzzle. The obtained results showed that an intelligent genetic algorithm can be used to solve cross-matching puzzles.
Similar content being viewed by others
References
Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley, Reading
Herrera F, Lozano M, Verdegay JL (1998) Tackling real-coded genetic algorithms: operators and tools for behavioural analysis. Artif Intell Rev 12(4):265–319
Holland JH (1975) Adaptation in natural and artificial systems. The University of Michigan press, Ann Arbor
Kesemen O, Karakaya G (2010a) A new game of numbers placement: frequency puzzle (SIKBUL), 9. National mathematics symposium, 20–22 Oct, Trabzon (in Turkish)
Kesemen O, Karakaya G (2010b) SIKBUL (frequency puzzles). Derya Inc., Trabzon (in Turkish)
Kesemen O, Özkul E (2012) Solving crossmatching puzzles using multi-layer genetic algorithms, first international conference on analysis and applied mathematics, 18–21 Oct, Gumushane
Larrañaga P, Kuijpers CMH, Murga RH, Inza I, Dizdarevic S (1999) Genetic algorithms for the travelling salesman problem: a review of representations and operators. Artif Intell Rev 13(2):129–170
Lim TY (2014) Structured population genetic algorithms: a literature survey. Artif Intell Rev 41(3):385–399
Lim YC, Tan TS, Salleh SHS, Ling DK (2012) Application of genetic algorithm in unit selection for Malay speech synthesis system. Expert Syst Appl 39(5):5376–5383
Mantere T, Koljonen J (2006) Solving and Rating sudoku puzzles with genetic algorithms, new developments in artificial intelligence and the semantic web. In: Proceedings of the 12th finnish artificial intelligence conference step
Mitchell M (1998) An introduction to genetic algorithm. MIT press, Cambridge
Tsai JT, Chou PY, Fang JC (2012) Learning intelligent genetic algorithms using Japanese nonograms. IEEE Trans Educ 55(2):164–168
Author information
Authors and Affiliations
Corresponding author
Appendix: Algorithms
Appendix: Algorithms
Rights and permissions
About this article
Cite this article
Kesemen, O., Özkul, E. Solving cross-matching puzzles using intelligent genetic algorithms. Artif Intell Rev 49, 211–225 (2018). https://doi.org/10.1007/s10462-016-9522-6
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10462-016-9522-6