Abstract
The development of education and college expansion and consolidation in the Educational Management System has made the Course Scheduling System complex, and therefore it has become necessary to design one for development, and reuse. A Course Timetabling Problem (CTP) is an NP-hard combinatorial optimization problem which lacks analytical solution methods. During the last two decades several algorithms have been proposed, most of which are based on heuristics like evolutionary computation methods. This paper proposes a solution based on genetic algorithm .Genetic Algorithm (GA) emerges as one automation timetabling method to solve this problem by searching solution in multi-points and the ability to refine and optimizing the existing solution to a better solution. The experimental results show that the proposed GAs are able to produce promising results for the course timetabling problem.
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Yang, N.-N., Ni, J.: Genetic algorithm and its application in scheduling system. TELKOMNIKA 11(1), 1934–1939 (2013)
Ramik, J., Perzina, R.: Self-learning genetic algorithm for a timetabling problem with fuzzy constraints. International Journal of Innovative Computing, Information and Control 9(11), 4565–4582 (2013)
Sharma, R., Mehta, K., Kumar, K., Sikander: Genetic algorithm approach to automate university timetable. International Journal of Technical Research 1(1) (March 2012)
Yang, Y., Petrovic, S., Dror, M.: Case-based selection of initialisation heuristics for metaheuristic examination timetabling. Expert Systems with Applications 33(3), 772–785 (2007)
Legierski, W.: Search strategy for constraint-based classteacher timetabling. In: Burke, E.K., De Causmaecker, P. (eds.) PATAT 2002. LNCS, vol. 2740, pp. 247–261. Springer, Heidelberg (2003)
Belaton, B., Thomas, J.J., Khader, A.T.: A visual analytics framework for the examination timetabling problem. In: Proceedings of the Fifth International Conference on Computer Graphics, Imaging and Visualisation, vol. 1(1), pp. 305–310 (August 2008)
Aycan, E., Ayav, T.: Solving the course scheduling problem using simulated annealing. In: IEEE International Advance Computing Conference, IACC 2009, vol. 1(1), pp. 462–466 (2009)
Hao, J.-K., Lu, Z.: Adaptive tabu search for course timetabling. European Journal of Operational Research 200(1), 235–244 (2010)
Zhu, L., Guo, P., Chen, J.-X.: The design and implementation of timetable system based on genetic algorithm. In: International Conference on Mechatronic Science, Electric Engineering and Computer, vol. 1(1) (August 2011)
Chai, S., Sabri, M.F.M., Husin, M.H.: Development of a timetabling software using soft-computing techniques with a case study. In: The 2nd International Conference on Computer and Automation Engineering (ICCAE), vol. 5(1), pp. 394–397 (February 2010)
Lassig, J., Hoffmann, K.H.: On the structure of a best possible crossover selection strategy in genetic algorithms. In: Research and Development in Intelligent Systems XXVI, vol. 26, pp. 263–276. Springer (April 2010)
Withall, M.S., Jackson, T.W., Phillips, I.W., Brown, S., Clarke, M., Hinde, C.J., Watson, R.: Allocating railway platforms using a genetic algorithm. In: Research and Development in Intelligent Systems XXVI, vol. 26, pp. 421–434. Springer (April 2010)
Binu, D., George, A., Rajakumar, B.R.: Genetic algorithm based airlines booking terminal open/close decision system. In: ICACCI 2012, vol. 1(1), ACM (August 2012)
Melanie, M.: An Introduction to Genetic Algorithms. First MIT Press paperback edition (1998)
Luke, S.: Essentials of Metaheuristics Online version (June 2013)
Haupt, R.L., Haupt, S.E.: Practical Genetic Algorithms, 2nd edn. John Wiley & Sons, Inc. (2004)
de Oliveira Rech, L., Lung, L.C., Ribeiro, G.O., de Campos, A.J.R.: Generating timetables of work scales for companies using genetic algorithms. In: 2012 XXXVIII Conferencia Latinoamericana En IEEE Informatica (CLEI), vol. 1(1), pp. 1–10 (October 2012)
Adachi, Y., Ataka, S.: Study on timetable design for osaka international university by differential evolution. In: The 1st IEEE Global Conference on Consumer Electronics, vol. 1(1), pp. 1–10 (2012)
Cooper, T.B., Kingston, J.H.: The complexity of timetable construction problems. University of Sydney, Technical report, vol. 495(1) (February 1995)
Beaty, S.J.: Genetic Algorithms versus Tabu Search for Instruction Scheduling. Artificial Neural Nets and Genetic Algorithms 1, 496–501 (1993)
Aladağ, C.H., Hocaoglu, G.: A Tabu Search Algorithm to Solve a Course Timetabling Problem. Hacettepe Journal of Mathematics and Statistics 36(1), 53–64 (2007)
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Limkar, S., Khalwadekar, A., Tekale, A., Mantri, M., Chaudhari, Y. (2015). Genetic Algorithm: Paradigm Shift over a Traditional Approach of Timetable Scheduling. In: Satapathy, S., Biswal, B., Udgata, S., Mandal, J. (eds) Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2014. Advances in Intelligent Systems and Computing, vol 327. Springer, Cham. https://doi.org/10.1007/978-3-319-11933-5_87
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DOI: https://doi.org/10.1007/978-3-319-11933-5_87
Publisher Name: Springer, Cham
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