Advertisement

An Informed Genetic Algorithm for University Course and Student Timetabling Problems

Part of the Lecture Notes in Computer Science book series (LNCS, volume 6114)

Abstract

This paper describes an Informed Genetic Algorithm (IGA), a genetic algorithm using greedy initialization and directed mutation, to solve a practical university course and student timetabling problem. A greedy method creates some feasible solutions, where all specified hard constraints are not broken, as initial population. A directed mutation scheme is used to reduce violations regarding all given soft constraints and to keep the solutions feasible. Here, IGA creates a timetable in two stages. Firstly, IGA evolves a course timetable using any constraints regarding lecturer, class and room. This stage produce best-so-far timetable. Secondly, using some certain rules IGA evolves the best-so-far timetable using all constraints. The batch student sectioning is done by allowing the first stage timetable to change. Computer simulation to a highly constrained timetabling problem shows that the informed GA is capable of producing a reliable timetable.

Keywords

Time Slot Soft Constraint Hard Constraint Timetabling Problem Directed Mutation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    Müller, T., Murray, K.: Comprehensive Approach to Student Sectioning. In: The 7th International Conference on the Practice and Theory of Automated Timetabling (2008)Google Scholar
  2. [2]
    Nuntasen, N., Innet, S.: A Novel Approach of Genetic Algorithm for Solving University Timetabling Problems: a case study of Thai Universities. In: Proceedings of the 6th WSEAS International Conference on System Science and Simulation in Engineering (2007)Google Scholar
  3. [3]
    Murray, K., Müller, T., Rudová, H.: Modeling and Solution of a Complex University Course Timetabling Problem. In: The 6th International Conference on the Practice and Theory of Automated Timetabling (2007)Google Scholar
  4. [4]
    Burke, E.K., Jackson, K., Kingston, J.H., Weare, R.: Automated University Timetabling: The State of the Art. The Computer Journal 40(9), 565–571 (1997)CrossRefGoogle Scholar
  5. [5]
    Burke, E.K., Elliman, D., Weare, R.F.: A Hybrid Genetic Algorithm for Highly Constrained Timetabling Problems. In: Proceedings of the 6th International Conference on Genetic Algorithms, pp. 605–610 (1995)Google Scholar
  6. [6]
    Ross, P., Corne, D., Fang, H.-L.: Improving Evolutionary Timetabling with Delta Evaluation and Directed Mutation. In: Davidor, Y., Männer, R., Schwefel, H.-P. (eds.) PPSN 1994. LNCS, vol. 866, pp. 556–565. Springer, Heidelberg (1994)Google Scholar
  7. [7]
    Corne, D., Ross, P., Fang, H.-L.: Fast Practical Evolutionary Timetabling. In: Fogarty, T.C. (ed.) AISB-WS 1994. LNCS, vol. 865, pp. 251–263. Springer, Heidelberg (1994)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Suyanto
    • 1
  1. 1.Faculty of Informatics - IT TelkomWest JavaIndonesia

Personalised recommendations