An Empirical Analysis of the Grouping Genetic Algorithm: The Timetabling Case.

Lewis, R.; Paechter, B.

Abstract

A grouping genetic algorithm (GGA) for the university course timetabling problem is outlined. We propose six different fitness functions, all sharing the same common goal, and look at the effects that these can have on the algorithm with respect to both solution quality and time requirements. We also propose an additional, stochastic local-search operator and discover that this too can have large positive and negative effects on the runs. As a by-product of these studies, we introduce a method for measuring population diversity with the GGA model and note that diversity seems to have huge consequences on the cost implications of the algorithm. We also witness that the algorithm can behave quite differently with varying sized instances, introducing scaling-up issues that could, quite possibly, apply to grouping genetic algorithms as a whole.

Citation

Lewis, R., & Paechter, B. (2005). An Empirical Analysis of the Grouping Genetic Algorithm: The Timetabling Case. In 2005 IEEE Congress on Evolutionary Computation (2856-2863). https://doi.org/10.1109/cec.2005.1555053

• Presentation Conference Type: Conference Paper (Published)
• Conference Name: 2005 IEEE Congress on Evolutionary Computation
• Start Date: Sep 2, 2005
• End Date: Sep 5, 2005
• Publication Date: Dec 12, 2005
• Deposit Date: Apr 28, 2010
• Publicly Available Date: Apr 28, 2010
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Pages: 2856-2863
• Book Title: 2005 IEEE Congress on Evolutionary Computation
• ISBN: 0780393635
• DOI: https://doi.org/10.1109/cec.2005.1555053
• Keywords: Grouping Genetic Algorithm (GGA); timetabling; diversity; local-search operator
• Public URL: http://researchrepository.napier.ac.uk/id/eprint/3394
• Publisher URL: http://dx.doi.org/10.1109/CEC.2005.1555053
• Contract Date: Apr 28, 2010