Evolutionary algorithms using cluster patterns for timetabling

  • Authors:

  • Nandita Sharma;Tom D. Gedeon;B. Sumudu U. Mendis

  • Affiliations:

  • Information and Human Centred Computing Group, Research School of Computer Science, Australian National University, Canberra, ACT, Australia;Information and Human Centred Computing Group, Research School of Computer Science, Australian National University, Canberra, ACT, Australia;Information and Human Centred Computing Group, Research School of Computer Science, Australian National University, Canberra, ACT, Australia

  • Venue:
  • Intelligent Decision Technologies
  • Year:
  • 2013

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Abstract

The examination timetabling problem ETP is a NP complete, combinatorial optimization problem. Intuitively, use of properties such as patterns or clusters in the data suggests possible improvements in the performance and quality of timetabling. This paper investigates whether the use of a genetic algorithm GA informed by patterns extracted from student timetable data to solve ETPs can produce better quality solutions. The data patterns were captured in clusters, which then were used to generate the initial population and evaluate fitness of individuals. The proposed techniques were compared with a traditional GA and popular techniques on widely used benchmark problems, and a local data set, the Australian National University ANU ETP, which was the motivating problem for this work. A formal definition of the ANU ETP is also proposed. Results show techniques using cluster patterns produced better results than the traditional GA with statistical significance of p