Citation
A Guided Search Genetic Algorithm for the University Course Timetabling Problem. Proceedings of the 4th Multidisciplinary International Scheduling Conference: Theory and Applications (MISTA 2009), 10-12 Aug 2009, Dublin, Ireland, pages 180-191, 2009.
Paper
Abstract
The university course timetabling problem is a combinatorial optimisation problem in which a set of events has to be scheduled in time slots and located in suitable rooms. The design of course timetables for academic institutions is a very difficult task because it is an NP-hard problem. This paper proposes a genetic algorithm with a guided search strategy and a local search technique for the university course timetabling problem. The guided search strategy is used to create offspring into the population based on a data structure that stores information extracted from previous good individuals. The local search technique is used to improve the quality of individuals. The proposed genetic algorithm is tested on a set of benchmark problems in comparison with a set of state-of-the-art methods from the literature. The experimental results show that the proposed genetic algorithm is able to produce promising results for the university course timetabling problem.
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Bibtex
@INPROCEEDINGS{2009-180-191-P, author = {S. N. Jat and S. Yang},
title = {A Guided Search Genetic Algorithm for the University Course Timetabling Problem},
booktitle = {Proceedings of the 4th Multidisciplinary International Scheduling Conference: Theory and Applications (MISTA 2009), 10-12 Aug 2009, Dublin, Ireland},
year = {2009},
editor = {J. Blazewicz and M. Drozdowski and G. Kendall and B. McCollum},
pages = {180--191},
note = {Paper},
abstract = {The university course timetabling problem is a combinatorial optimisation problem in which a set of events has to be scheduled in time slots and located in suitable rooms. The design of course timetables for academic institutions is a very difficult task because it is an NP-hard problem. This paper proposes a genetic algorithm with a guided search strategy and a local search technique for the university course timetabling problem. The guided search strategy is used to create offspring into the population based on a data structure that stores information extracted from previous good individuals. The local search technique is used to improve the quality of individuals. The proposed genetic algorithm is tested on a set of benchmark problems in comparison with a set of state-of-the-art methods from the literature. The experimental results show that the proposed genetic algorithm is able to produce promising results for the university course timetabling problem.},
owner = {gxk},
timestamp = {2010.10.11},
webpdf = {2009-180-191-P.pdf} }