Generic Memetic Algorithm for Course Timetabling ITC2007

  • Soria-Alcaraz Jorge
  • Carpio Martin
  • Puga Hector
  • Melin Patricia
  • Terashima-Marin Hugo
  • Cruz Laura
  • Sotelo-Figueroa Marco
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 547)

Abstract

Course timetabling is an important and recurring administrative activity in most educational institutions. This chapter describes an automated configuration of a generic memetic algorithm to solving this problem. This algorithm shows competitive results on well-known instances compared against top participants of the most recent International ITC2007 Timetabling Competition. Importantly, our study illustrates a case where generic algorithms with increased autonomy and generality achieve competitive performance against human designed problem-specific algorithms.

Keywords

Methodology of design Course timetabling ITC2007 Generic framework Memetic algorithms 

Notes

Acknowledgments

Authors thanks the support received from the Consejo Nacional de Ciencia y Tecnologia (CONACYT) México and The University of Stirling UK.

References

  1. 1.
    Cooper, T.B., Kingston, J.H.: The complexity of timetable construction problems. Ph.D. Thesis, The University of Sydney (1995)Google Scholar
  2. 2.
    Willemen, R.J.: School timetable construction: algorithms and complexity. Ph.D. Thesis, Institute for Programming Research and Algorithms (2002)Google Scholar
  3. 3.
    Lewis, R.: Metaheuristics for University course timetabling. Ph.D. thesis, University of Nottingham (2006)Google Scholar
  4. 4.
    Ong, Y.S., et al.: Classification of adaptive memetic algorithms: a comparative study. Syst. Man Cybern. Part B: Cybern. IEEE Trans. 36(1), 141–152 (2006)CrossRefGoogle Scholar
  5. 5.
    Radcliffe, et al. 1994. Formal memetic algorithms, vol. 85. Evolutionary Computing. Lecture Notes in Computer Science, pp. 1–16. Springer, BerlinGoogle Scholar
  6. 6.
    Soria-Alcaraz Jorge, A., et al.: Methodology of design: a novel generic approach applied to the course timetabling problem, vol. 451. Studies in Computational Intelligence. Springer Berlin (2013b)Google Scholar
  7. 7.
    Soria-Alcaraz Jorge, A., et al.: Comparison of Metaheuristic algorithms with a methodology of design for the evaluation of hard constraints over the course timetabling problem, vol. 451. Studies in Computational Intelligence. Springer, Berlin (2013a)Google Scholar
  8. 8.
    de Werra, D.: An introduction to timetabling. Eur. J. Oper. Res. 19(2), 151–162 (1985)CrossRefMATHGoogle Scholar
  9. 9.
    Carter, M.: A survey of practical applications of examination timetabling algorithms. Oper. Res. 34, 193–202 (1986)CrossRefMathSciNetGoogle Scholar
  10. 10.
    Lajos, G.: Complete university modular timetabling using constraint logic programming. In E Burke and P Ross editors. Practice and Theory of Automated Timetabling (PATAT), vol. 1153, pp. 146–161. Springer, Berlin (1996)Google Scholar
  11. 11.
    Boizumault, P., et al.: Logic programming for examination timetabling. Logic Program 26, 217–233 (1996)CrossRefMATHGoogle Scholar
  12. 12.
    Lü, Z., Hao, J.-K.: Adaptive Tabu Search for course timetabling. Eur. J. Oper. Res. 200(1), 235–244 (2010)CrossRefMATHGoogle Scholar
  13. 13.
    Colorni, A., et al.: Metaheuristics for high-school timetabling. Comput. Optim. Appl. 9, 277–298 (1997)Google Scholar
  14. 14.
    Yu, E., Sung, K.S.: A genetic algorithm for a University Wekly courses timetabling problem. Int. Trans. Oper. Res. 9, 703–717 (2002)CrossRefMATHGoogle Scholar
  15. 15.
    Nothegger, C., Mayer, A., Chwatal, A., & Raidl, G. R.: Solving the post enrolment course timetabling problem by ant colony optimization. Annals of Operations Research. 194(1), 325–339 (2012) Google Scholar
  16. 16.
    Socha, K., et al.: A MAX-MIN Ant system for the University Course timetabling Problem. In: Dorigo, M., Caro, G.D., Samples, M. (eds.) Proceedings of Ants 2002—Third international workshop on Ant algorithms, Lecture Notes in Computer Science, pp. 1–13. Springer, Berlin (2002)Google Scholar
  17. 17.
    Burke, E., et al.: Hybrid variable neighbourhood approaches to university exam timetabling. Eur. J. Oper. Res. 206(1), 46–53 (2010)CrossRefMATHMathSciNetGoogle Scholar
  18. 18.
    Sabar, N.R., et al.: A honey-bee mating optimization algorithm for educational timetabling problems. Eur. J. Oper. Res. 216(3), 533–543 (2012)CrossRefMathSciNetGoogle Scholar
  19. 19.
    Thompson, J.M., Dowsland, K.A.: A robust simulated annealing based examination timetabling system. Comput. Oper. Res. 25, 637–648 (1998)CrossRefMATHGoogle Scholar
  20. 20.
    Rudova, H., et al.: Complex university course timetabling. J. Sched. 14, 187–207 (2011). doi: 10.1007/s10951-010-0171-3 CrossRefMathSciNetGoogle Scholar
  21. 21.
    Cambazard, H., et al.: Local search and constraint programming for the post enrolment-based course timetabling problem. Ann. Oper. Res. 194, 111–135 (2012)CrossRefMATHGoogle Scholar
  22. 22.
    Burke, E.K., et al.: A graph-based hyper-heuristic for educational timetabling problems. Eur. J. Oper. Res. 176(1), 177–192 (2007)CrossRefMATHMathSciNetGoogle Scholar
  23. 23.
    Soria-Alcaraz, J.A., et al.: Academic timetabling design using hyper-heuristics. Adv. Soft Comput. 1, 158–164 (2010). ITT Springer-VerlagGoogle Scholar
  24. 24.
    Causmaecker, P.D., et al.: A decomposed metaheuristic approach for a real-world university timetabling problem. Eur. J. Oper. Res. 195(1), 307–318 (2009)CrossRefMATHGoogle Scholar
  25. 25.
    Kahar, M., Kendall, G.: The examination timetabling problem at Universiti Malaysia Pahang: comparison of a constructive heuristic with an existing software solution. Eur. J. Oper. Res. 207(2), 557–565 (2010)CrossRefMATHGoogle Scholar
  26. 26.
    Conant-Pablos, S.E., et al.: Pipelining Memetic algorithms, constraint satisfaction, and local search for course timetabling. MICAI Mexican International Conference on Artificial Intelligence, vol. 1, pp 408–419. (2009)Google Scholar
  27. 27.
    Hoos, H.H.: Automated Algorithm Configuration and Parameter Tuning, Chap. 3, pp. 37–71. Springer, Berlin (2012)Google Scholar
  28. 28.
    Hutter, F., et al.: ParamILS: an automatic algorithm configuration framework. J. Artif. Intell. Res. 36(1), 267–306 (2009)MATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Soria-Alcaraz Jorge
    • 1
  • Carpio Martin
    • 1
  • Puga Hector
    • 1
  • Melin Patricia
    • 2
  • Terashima-Marin Hugo
    • 3
  • Cruz Laura
    • 4
  • Sotelo-Figueroa Marco
    • 1
  1. 1.Division de Estudios de Posgrado e InvestigacionLeon Institute of TechnologyLeónMexico
  2. 2.Tijuana Institute of TechnologyTijuana B.CMexico
  3. 3.Instituto de Estudios Superiores de Monterrey ITESMMonterrey N.LMexico
  4. 4.Instituto Tecnologico de Cd. MaderoMadero TamaulipasMexico

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