Comparison of bioinspired algorithms applied to the timetabling problem
The problem of timetabling events is present in various organizations such as schools, hospitals, transportation centers. The purpose of timetabling activities at a university is to ensure that all students attend their required subjects in accordance with the available resources. The set of constra...
- Autores:
-
Silva, Jose
Varela Izquierdo, Noel
Varas, Jesus
Lezama, Omar
Maco, José
Villón, Martín
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2021
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/7717
- Acceso en línea:
- https://hdl.handle.net/11323/7717
https://doi.org/10.1007/978-981-15-7907-3_32
https://repositorio.cuc.edu.co/
- Palabra clave:
- Genetic algorithm
Memetic algorithm
Immune system
Faculty timetabling
Course timetabling
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 International
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Comparison of bioinspired algorithms applied to the timetabling problem |
| title |
Comparison of bioinspired algorithms applied to the timetabling problem |
| spellingShingle |
Comparison of bioinspired algorithms applied to the timetabling problem Genetic algorithm Memetic algorithm Immune system Faculty timetabling Course timetabling |
| title_short |
Comparison of bioinspired algorithms applied to the timetabling problem |
| title_full |
Comparison of bioinspired algorithms applied to the timetabling problem |
| title_fullStr |
Comparison of bioinspired algorithms applied to the timetabling problem |
| title_full_unstemmed |
Comparison of bioinspired algorithms applied to the timetabling problem |
| title_sort |
Comparison of bioinspired algorithms applied to the timetabling problem |
| dc.creator.fl_str_mv |
Silva, Jose Varela Izquierdo, Noel Varas, Jesus Lezama, Omar Maco, José Villón, Martín |
| dc.contributor.author.spa.fl_str_mv |
Silva, Jose Varela Izquierdo, Noel Varas, Jesus Lezama, Omar Maco, José Villón, Martín |
| dc.subject.spa.fl_str_mv |
Genetic algorithm Memetic algorithm Immune system Faculty timetabling Course timetabling |
| topic |
Genetic algorithm Memetic algorithm Immune system Faculty timetabling Course timetabling |
| description |
The problem of timetabling events is present in various organizations such as schools, hospitals, transportation centers. The purpose of timetabling activities at a university is to ensure that all students attend their required subjects in accordance with the available resources. The set of constraints that must be considered in the design of timetables involves students, teachers and infrastructure. This study shows that acceptable solutions are generated through the application of genetic, memetic and immune system algorithms for the problem of timetabling. The algorithms are applied to real instances of the University of Mumbai in India and their results are comparable with those of a human expert. |
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2021 |
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2021-01-19T20:36:32Z |
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2021-01-19T20:36:32Z |
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2021 |
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Artículo de revista |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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https://doi.org/10.1007/978-981-15-7907-3_32 |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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https://repositorio.cuc.edu.co/ |
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Corporación Universidad de la Costa REDICUC - Repositorio CUC |
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eng |
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eng |
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1. Jorge AS, Martin CJ, Hugo T (2010) Academic timetabling design using hyper—heuristics. Springer, Berlin, pp 43–56 2. Asratian AS, de Werra D (2002) A generalized class–teacher model for some timetabling problems. University of Technology, Department of Engineering Sciences and Mathematics, Mathematical Science, & Mathematics. Eur J Oper Res 531–542 3. Soria-Alcaraz Jorge A, Martín C, Héctor P, Sotelo-Figueroa MA 2013) Comparison of metaheuristic algorithms with a methodology of design for the evaluation of hard constraints over the course timetabling problem. Springer, Berlin, pp 289–302. 4. Viloria A, Lis-Gutiérrez JP, Gaitán-Angulo M, Godoy ARM, Moreno GC, Kamatkar SJ (2018) Methodology for the design of a student pattern recognition tool to facilitate the teaching—learning process through knowledge data discovery (big data). In: Tan Y, Shi Y, Tang Q (eds) Data mining and big data. DMBD 2018. 5. De Werra D (1985) An introduction to timetabling. Eur J Oper Res 19(2):151–162 6. Obit JH, Ouelhadj D, Landa-Silva D, Vun TK, Alfred R (2011) Designing a multi-agent approach system for distributed course timetabling, pp 103–108. 7. Lewis MRR (2006) Metaheuristics for university course timetabling. Ph.D. Thesis, Napier University 8. Deng X, Zhang Y, Kang B, Wu J, Sun X, Deng Y (2011) An application of genetic algorithm for university course timetabling problem, pp 2119–2122./ 9. Mahiba AA, Durai CAD (2012) Genetic algorithm with search bank strategies for university course timetabling problem. Procedia Eng 38:253–263 10. Kamatkar SJ, Kamble A, Viloria A, Hernández-Fernandez L, Cali EG (2018) Database performance tuning and query optimization. In: International conference on data mining and big data. Springer, Cham, pp 3–11 11. Nguyen K, Lu T, Le T, Tran N (2011) Memetic algorithm for a university course timetabling problem, pp. 67–71. 12. Aladag C, Hocaoglu G (2007) A tabu search algorithm to solve a course timetabling problem. Hacettepe J Math Stat, pp 53–64 13. Moscato P (1989) On evolution, search, optimization, genetic algorithms and martial arts: towards memetic algorithms. Caltech Concurrent Computation Program (report 826) 14. Frausto-Solís J, Alonso-Pecina F, Mora-Vargas J (2008) An efficient simulated annealing algorithm for feasible solutions of course timetabling. Springer, Berlin, pp 675–685 15. Joudaki M, Imani M, Mazhari N (2010) Using improved memetic algorithm and local search to solve university course timetabling problem (UCTTP). Islamic Azad University, Doroud 16. Thepphakorn T, Pongcharoen P, Hicks C (2014) An ant colony based timetabling tool. Int J Prod Econ 149:131–144. 17. Soria-Alcaraz J, Ochoa G, Swan J, Carpio M, Puga H, Burke E (2014) Effective learning hyper-heuristics for the course timetabling problem. Eur J Oper Res 77–86. 18. Wolpert H, Macready G (1996) No free lunch theorems for search. Technical report, The Santa Fe Institute, vol 1 19. Lai LF, Wu C, Hsueh N, Huang L, Hwang S (2008) An artificial intelligence approach to course timetabling. Int J Artif Intell Tools 223–240. 20. McCollum B, McMullan P, Parkes AJ, Burke EK, Qu R (2012) A new model for automated examination timetabling. Ann Oper Res 291–315 21. Conant-Pablos SE et al (2009) Pipelining memetic algorithms, constraint satisfaction, and local search for course timetabling. In: MICAI Mexican international conference on artificial intelligence, vol 1, pp 408–419 22. Carpio-Valadez JM (2006) Integral model for optimal assignation of academic tasks. In: Encuentro de investigacion en ingenieria electrica. ENVIE, Zacatecas, pp 78–83 23. Soria-Alcaraz JA, Martin C, Héctor P, Hugo T, Laura CR, Sotelo-Figueroa MA (2013) Methodology of design: a novel generic approach applied to the course timetabling problem, pp 287–319. 24. Talbi E (2009) Metaheuristics: from design to implementation. Wiley, US 25. Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley Pub. Co, Reading 26. Yang X-S (2010) Nature-inspired metaheuristic algorithms. Luniver Press 27. Abdoun O, Abouchabaka J (2011) A comparative study of adaptive crossover operators for genetic algorithms to resolve the traveling salesman problem. Int J Comput Appl 28. Derrac J, García S (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence. In: Swarm and Evolutionary Computation 29. Azuaje F (2003) Review of “Artificial immune systems: a new computational intelligence approach.” J Neural Netw 16(8):1229–1229 30. Maulik U, Bandyopadhyay S (2000) Genetic algorithm-based clustering technique. Pattern Recogn 33:1455–1465 31. Lü Z, Hao J (2010) Adaptive tabu search for course timetabling. Eur J Oper Res 235–244 32. Viloria A, Lezama OBP (2019) Improvements for determining the number of clusters in k-means for innovation databases in SMEs. Procedia Comput Sci 151:1201–1206 |
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Silva, Jose1d7ce9bcd08d6be0ee4300755e53d888Varela Izquierdo, Noel484160b66adc1de7303e235ec7894532Varas, Jesusf12809b7c3639c2ed486a652d44c6c26Lezama, Omard3cb35441d7a84a4bdb5e934cbf4f5afMaco, José3ae5a9f0859c736faaf0c56a1aaec025Villón, Martínf63f5e5ca7d5f396ac1e41161469c7c02021-01-19T20:36:32Z2021-01-19T20:36:32Z2021https://hdl.handle.net/11323/7717https://doi.org/10.1007/978-981-15-7907-3_32Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The problem of timetabling events is present in various organizations such as schools, hospitals, transportation centers. The purpose of timetabling activities at a university is to ensure that all students attend their required subjects in accordance with the available resources. The set of constraints that must be considered in the design of timetables involves students, teachers and infrastructure. This study shows that acceptable solutions are generated through the application of genetic, memetic and immune system algorithms for the problem of timetabling. The algorithms are applied to real instances of the University of Mumbai in India and their results are comparable with those of a human expert.application/pdfengCorporación Universidad de la CostaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Advances in Intelligent Systems and Computinghttps://link.springer.com/chapter/10.1007/978-981-15-7907-3_32Genetic algorithmMemetic algorithmImmune systemFaculty timetablingCourse timetablingComparison of bioinspired algorithms applied to the timetabling problemArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion1. Jorge AS, Martin CJ, Hugo T (2010) Academic timetabling design using hyper—heuristics. Springer, Berlin, pp 43–562. Asratian AS, de Werra D (2002) A generalized class–teacher model for some timetabling problems. University of Technology, Department of Engineering Sciences and Mathematics, Mathematical Science, & Mathematics. Eur J Oper Res 531–5423. Soria-Alcaraz Jorge A, Martín C, Héctor P, Sotelo-Figueroa MA 2013) Comparison of metaheuristic algorithms with a methodology of design for the evaluation of hard constraints over the course timetabling problem. Springer, Berlin, pp 289–302.4. Viloria A, Lis-Gutiérrez JP, Gaitán-Angulo M, Godoy ARM, Moreno GC, Kamatkar SJ (2018) Methodology for the design of a student pattern recognition tool to facilitate the teaching—learning process through knowledge data discovery (big data). In: Tan Y, Shi Y, Tang Q (eds) Data mining and big data. DMBD 2018.5. De Werra D (1985) An introduction to timetabling. Eur J Oper Res 19(2):151–1626. Obit JH, Ouelhadj D, Landa-Silva D, Vun TK, Alfred R (2011) Designing a multi-agent approach system for distributed course timetabling, pp 103–108.7. Lewis MRR (2006) Metaheuristics for university course timetabling. Ph.D. Thesis, Napier University8. Deng X, Zhang Y, Kang B, Wu J, Sun X, Deng Y (2011) An application of genetic algorithm for university course timetabling problem, pp 2119–2122./9. Mahiba AA, Durai CAD (2012) Genetic algorithm with search bank strategies for university course timetabling problem. Procedia Eng 38:253–26310. Kamatkar SJ, Kamble A, Viloria A, Hernández-Fernandez L, Cali EG (2018) Database performance tuning and query optimization. In: International conference on data mining and big data. Springer, Cham, pp 3–1111. Nguyen K, Lu T, Le T, Tran N (2011) Memetic algorithm for a university course timetabling problem, pp. 67–71.12. Aladag C, Hocaoglu G (2007) A tabu search algorithm to solve a course timetabling problem. Hacettepe J Math Stat, pp 53–6413. Moscato P (1989) On evolution, search, optimization, genetic algorithms and martial arts: towards memetic algorithms. Caltech Concurrent Computation Program (report 826)14. Frausto-Solís J, Alonso-Pecina F, Mora-Vargas J (2008) An efficient simulated annealing algorithm for feasible solutions of course timetabling. Springer, Berlin, pp 675–68515. Joudaki M, Imani M, Mazhari N (2010) Using improved memetic algorithm and local search to solve university course timetabling problem (UCTTP). Islamic Azad University, Doroud16. Thepphakorn T, Pongcharoen P, Hicks C (2014) An ant colony based timetabling tool. Int J Prod Econ 149:131–144.17. Soria-Alcaraz J, Ochoa G, Swan J, Carpio M, Puga H, Burke E (2014) Effective learning hyper-heuristics for the course timetabling problem. Eur J Oper Res 77–86.18. Wolpert H, Macready G (1996) No free lunch theorems for search. Technical report, The Santa Fe Institute, vol 119. Lai LF, Wu C, Hsueh N, Huang L, Hwang S (2008) An artificial intelligence approach to course timetabling. Int J Artif Intell Tools 223–240.20. McCollum B, McMullan P, Parkes AJ, Burke EK, Qu R (2012) A new model for automated examination timetabling. Ann Oper Res 291–31521. Conant-Pablos SE et al (2009) Pipelining memetic algorithms, constraint satisfaction, and local search for course timetabling. In: MICAI Mexican international conference on artificial intelligence, vol 1, pp 408–41922. Carpio-Valadez JM (2006) Integral model for optimal assignation of academic tasks. In: Encuentro de investigacion en ingenieria electrica. ENVIE, Zacatecas, pp 78–8323. Soria-Alcaraz JA, Martin C, Héctor P, Hugo T, Laura CR, Sotelo-Figueroa MA (2013) Methodology of design: a novel generic approach applied to the course timetabling problem, pp 287–319.24. Talbi E (2009) Metaheuristics: from design to implementation. Wiley, US25. Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley Pub. Co, Reading26. Yang X-S (2010) Nature-inspired metaheuristic algorithms. Luniver Press27. Abdoun O, Abouchabaka J (2011) A comparative study of adaptive crossover operators for genetic algorithms to resolve the traveling salesman problem. Int J Comput Appl28. Derrac J, García S (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence. In: Swarm and Evolutionary Computation29. Azuaje F (2003) Review of “Artificial immune systems: a new computational intelligence approach.” J Neural Netw 16(8):1229–122930. Maulik U, Bandyopadhyay S (2000) Genetic algorithm-based clustering technique. Pattern Recogn 33:1455–146531. Lü Z, Hao J (2010) Adaptive tabu search for course timetabling. Eur J Oper Res 235–24432. Viloria A, Lezama OBP (2019) Improvements for determining the number of clusters in k-means for innovation databases in SMEs. 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