Solving cross-matching puzzles using intelligent genetic algorithms
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 con...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Kesemen, Orhan [verfasserIn] |
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Artikel |
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Englisch |
| Erschienen: |
2016 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media Dordrecht 2016 |
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| Übergeordnetes Werk: |
Enthalten in: Artificial intelligence review - Springer Netherlands, 1987, 49(2016), 2 vom: 19. Okt., Seite 211-225 |
|---|---|
| Übergeordnetes Werk: |
volume:49 ; year:2016 ; number:2 ; day:19 ; month:10 ; pages:211-225 |
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10.1007/s10462-016-9522-6 |
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| 520 | |a 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. | ||
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10.1007/s10462-016-9522-6 doi (DE-627)OLC2066034177 (DE-He213)s10462-016-9522-6-p DE-627 ger DE-627 rakwb eng 004 VZ 54.00 bkl Kesemen, Orhan verfasserin aut Solving cross-matching puzzles using intelligent genetic algorithms 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media Dordrecht 2016 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. Genetic algorithm Depth first search Cross-matching puzzle Multi-layer genetic algorithm Intelligent genetic algorithm Özkul, Eda aut Enthalten in Artificial intelligence review Springer Netherlands, 1987 49(2016), 2 vom: 19. Okt., Seite 211-225 (DE-627)129223018 (DE-600)56633-0 (DE-576)014458209 0269-2821 nnns volume:49 year:2016 number:2 day:19 month:10 pages:211-225 https://doi.org/10.1007/s10462-016-9522-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_4046 GBV_ILN_4319 GBV_ILN_4323 54.00 VZ AR 49 2016 2 19 10 211-225 |
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10.1007/s10462-016-9522-6 doi (DE-627)OLC2066034177 (DE-He213)s10462-016-9522-6-p DE-627 ger DE-627 rakwb eng 004 VZ 54.00 bkl Kesemen, Orhan verfasserin aut Solving cross-matching puzzles using intelligent genetic algorithms 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media Dordrecht 2016 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. Genetic algorithm Depth first search Cross-matching puzzle Multi-layer genetic algorithm Intelligent genetic algorithm Özkul, Eda aut Enthalten in Artificial intelligence review Springer Netherlands, 1987 49(2016), 2 vom: 19. Okt., Seite 211-225 (DE-627)129223018 (DE-600)56633-0 (DE-576)014458209 0269-2821 nnns volume:49 year:2016 number:2 day:19 month:10 pages:211-225 https://doi.org/10.1007/s10462-016-9522-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_4046 GBV_ILN_4319 GBV_ILN_4323 54.00 VZ AR 49 2016 2 19 10 211-225 |
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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. © Springer Science+Business Media Dordrecht 2016 |
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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. © Springer Science+Business Media Dordrecht 2016 |
| abstract_unstemmed |
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. © Springer Science+Business Media Dordrecht 2016 |
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Solving cross-matching puzzles using intelligent genetic algorithms |
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