Lewis, Rhydian M R and Paechter, Ben (2005) An empirical analysis of the Grouping Genetic Algorithm: the timetabling case. In: 2005 IEEE Congress on Evolutionary Computation. IEEE Computer Society Press, Edinburgh, Scotland, pp. 2856-2863. ISBN 0-7803-9363-5
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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.
|Item Type:||Book Section|
|Uncontrolled Keywords:||Grouping Genetic Algorithm (GGA); timetabling; diversity; local-search operator;|
|University Divisions/Research Centres:||Faculty of Engineering, Computing and Creative Industries > School of Computing|
|Dewey Decimal Subjects:||500 Science > 510 Mathematics > 518 Numerical analysis|
|Library of Congress Subjects:||Q Science > QA Mathematics > QA75 Electronic computers. Computer science|
|Depositing User:||Computing Research|
|Date Deposited:||28 Apr 2010 13:27|
|Last Modified:||19 Dec 2013 14:41|
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