A Developmental Approach to the Uncapacitated Examination Timetabling Problem

  • Nelishia Pillay
  • Wolfgang Banzhaf
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5199)


The paper describes a new approach, based on cell biology, to the uncapacitated examination timetabling problem. This approach begins with a single cell which is developed into a fully grown organism through the processes of cell division, cell interaction and cell migration. The mature organism represents a solution to the particular timetabling problem. The paper discusses the performance of this method on the Carter set of benchmark problems. This data set is comprised of real-world timetabling problems. The results obtained using the developmental approach are compared to that obtained by other biologically inspired algorithms applied to the same set of benchmarks and the best results cited in the literature for the Carter data set.


biologically inspired algorithms uncapacitated examination timetabling problem 


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  1. 1.
    Azimi, Z.N.: Comparison of Metaheuristic Algorithms for Examination Timetabling Problem. Journal of Mathematics and Computing 16, 337–354 (2004)CrossRefzbMATHMathSciNetGoogle Scholar
  2. 2.
    Burke, E.K., Elliman, D., Weare, R.: A Genetic Algorithm Based University Timetabling System. In: Proceedings of the 2nd East-West International Conference on Computers in Education, vol. 1, pp. 35–40 (1994)Google Scholar
  3. 3.
    Burke, E.K., Newall, J.P., Weare, R.F.: A Memetic Algorithm for University Timetabling. In: Burke, E.K., Ross, P. (eds.) PATAT 1995. LNCS, vol. 1153, pp. 241–250. Springer, Heidelberg (1996)CrossRefGoogle Scholar
  4. 4.
    Burke, E.K., Bykov, Y.: Solving Exam Timetabling Problems with the Flex-Deluge Algorithm. In: Burke, E.K., Rudova, H. (eds.) Proceedings of the International Conference on the Theory and and Practice of Automated Timetabling (PATAT 2006), pp. 370–372 (2006)Google Scholar
  5. 5.
    Burke, E.K., Eckersley, A., McCollum, B., Petrovic, B., Qu, R.: Hybrid Variable Neighborhood Approaches to University Exam Timetabling. Technical Report NOTTCS-TR-2006-2. School of Computer Science and Information Technology, Nottingham, UK (2006)Google Scholar
  6. 6.
    Caramia, M., Dell Olmo, P., Italiano, G.F.: Novel Local-Search-Based Approaches to University Examination Timetabling. INFORMS Journal of Computing 20(1), 86–99 (2008)CrossRefzbMATHMathSciNetGoogle Scholar
  7. 7.
    Chu, S.C., Fang, S.L.: Genetic Algorithms vs. Tabu Search in Timetable Scheduling. In: Proceedings of the 3rd International Conference on Knowledge-Based Intelligence Information Engineering Systems, pp. 492–495. IEEE Press, Los Alamitos (1999)Google Scholar
  8. 8.
    Cote, P., Wong, T., Sabourin, L.: Application of a Hybrid Multi-Objective Evolutionary Algorithm to the Uncapacitated Exam Proximity Problem. In: Burke, E.K., Trick, M. (eds.) PATAT 2004. LNCS, vol. 3616, pp. 108–121. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  9. 9.
    Eley, M.: Ant Algorithms for the Exam Timetabling Problem. In: Burke, E.K., Rudová, H. (eds.) PATAT 2007. LNCS, vol. 3867, pp. 364–382. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  10. 10.
    Erben, W., Song, P.Y.: A Hybrid Grouping Genetic Algorithm for Examination Timetabling. In: Burke, E.K., Trick, M. (eds.) PATAT 2004. LNCS, vol. 3616, pp. 487–490. Springer, Heidelberg (2005)Google Scholar
  11. 11.
    McCollum, B.: A Perspective on Bridging the Gap between Theory and Practice in University Timetabling. In: Burke, E.K., Rudová, H. (eds.) PATAT 2007. LNCS, vol. 3867, pp. 3–23. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  12. 12.
    Ozcan, E., Ersoy, E.: Final Exam Scheduler – FES. In: Proceedings of the 2005 IEEE Congress on Evolutionary Computation, vol. 2, pp. 1356–1363. IEEE Press, Los Alamitos (2005)CrossRefGoogle Scholar
  13. 13.
    Paquete, L.F., Fonseca, C.M.: A Study of Examination Timetabling with Multiobjective Evolutionary Algorithms. In: Proceedings of the 4th Metaheuristics Conference (MIC 2001), pp. 149–154 (2001)Google Scholar
  14. 14.
    Qu, R., Burke, E.K., McCollum, B., Merlot, L.T.G., Lee, S.Y.: A Survey of Search Methodologies and Automated Approaches for Examination Timetabling. Computer Science Technical Report No. NOTTCS-TR-2006-4, UK (2006)Google Scholar
  15. 15.
    Ross, P., Hart, E., Corne, D.: Some Observations about GA-Based Exam Timetabling. In: Burke, E.K., Carter, M. (eds.) PATAT 1997. LNCS, vol. 1408, pp. 115–130. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  16. 16.
    Shebani, K.: An Evolutionary Approach for the Examination Timetabling Problems. In: Burke, E.K., De Causmaecker, P. (eds.) Proceedings of the 4th International Conference on the Theory and Practice for Automated Timetabling, pp. 387–396 (2002)Google Scholar
  17. 17.
    Ulker, O., Ozcan, E., Korkmaz, E.: Linear Linkage Encoding in Grouping Problems. In: Burke, E.K., Rudová, H. (eds.) PATAT 2007. LNCS, vol. 3867, pp. 347–363. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  18. 18.
    Wong, T., Cote, P., Gely, P.: Final Exam Timetabling: A Practical Approach. In: IEEE Canadian Conference on Electrical and Computer Engineering (CCECE 2002), vol. 2, pp. 726–731. IEEE Press, Los Alamitos (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Nelishia Pillay
    • 1
  • Wolfgang Banzhaf
    • 2
  1. 1.School of Computer ScienceUnivesity of KwaZulu-NatalPietermaritzburg, KwaZulu-NatalSouth Africa
  2. 2.Department of Computer ScienceMemorial University of NewfoundlandSt. John’sCanada

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