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An Informed Genetic Algorithm for University Course and Student Timetabling Problems

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Artifical Intelligence and Soft Computing (ICAISC 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6114))

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Abstract

This paper describes an Informed Genetic Algorithm (IGA), a genetic algorithm using greedy initialization and directed mutation, to solve a practical university course and student timetabling problem. A greedy method creates some feasible solutions, where all specified hard constraints are not broken, as initial population. A directed mutation scheme is used to reduce violations regarding all given soft constraints and to keep the solutions feasible. Here, IGA creates a timetable in two stages. Firstly, IGA evolves a course timetable using any constraints regarding lecturer, class and room. This stage produce best-so-far timetable. Secondly, using some certain rules IGA evolves the best-so-far timetable using all constraints. The batch student sectioning is done by allowing the first stage timetable to change. Computer simulation to a highly constrained timetabling problem shows that the informed GA is capable of producing a reliable timetable.

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References

  1. Müller, T., Murray, K.: Comprehensive Approach to Student Sectioning. In: The 7th International Conference on the Practice and Theory of Automated Timetabling (2008)

    Google Scholar 

  2. Nuntasen, N., Innet, S.: A Novel Approach of Genetic Algorithm for Solving University Timetabling Problems: a case study of Thai Universities. In: Proceedings of the 6th WSEAS International Conference on System Science and Simulation in Engineering (2007)

    Google Scholar 

  3. Murray, K., Müller, T., Rudová, H.: Modeling and Solution of a Complex University Course Timetabling Problem. In: The 6th International Conference on the Practice and Theory of Automated Timetabling (2007)

    Google Scholar 

  4. Burke, E.K., Jackson, K., Kingston, J.H., Weare, R.: Automated University Timetabling: The State of the Art. The Computer Journal 40(9), 565–571 (1997)

    Article  Google Scholar 

  5. Burke, E.K., Elliman, D., Weare, R.F.: A Hybrid Genetic Algorithm for Highly Constrained Timetabling Problems. In: Proceedings of the 6th International Conference on Genetic Algorithms, pp. 605–610 (1995)

    Google Scholar 

  6. Ross, P., Corne, D., Fang, H.-L.: Improving Evolutionary Timetabling with Delta Evaluation and Directed Mutation. In: Davidor, Y., Männer, R., Schwefel, H.-P. (eds.) PPSN 1994. LNCS, vol. 866, pp. 556–565. Springer, Heidelberg (1994)

    Google Scholar 

  7. Corne, D., Ross, P., Fang, H.-L.: Fast Practical Evolutionary Timetabling. In: Fogarty, T.C. (ed.) AISB-WS 1994. LNCS, vol. 865, pp. 251–263. Springer, Heidelberg (1994)

    Google Scholar 

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Authors and Affiliations

  1. Faculty of Informatics - IT Telkom, Jl. Telekomunikasi No. 1 Terusan Buah Batu Bandung, West Java, Indonesia

    Suyanto

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  1. Suyanto
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Editors and Affiliations

  1. Department of Artificial Intelligence, Academy of Humanities and Economics, Poland

    Leszek Rutkowski

  2. Academy of Humanities and Economics in Łódź, ul. Rewolucji 1905 nr 64, Łódź, Poland

    Rafał Scherer

  3. Institute of Automatics, AGH University of Science and Technology, Al. Mickiewicza 30, PL-30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  4. Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley Initiative in Soft Computing (BISC), 94720-1776, CA

    Lotfi A. Zadeh

  5. Computational Intelligence Laboratory Department of Electrical and Computer Engineering, University of Louisville, 40292, Louisville, KY

    Jacek M. Zurada

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Suyanto (2010). An Informed Genetic Algorithm for University Course and Student Timetabling Problems. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artifical Intelligence and Soft Computing. ICAISC 2010. Lecture Notes in Computer Science(), vol 6114. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13232-2_28

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  • DOI: https://doi.org/10.1007/978-3-642-13232-2_28

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13231-5

  • Online ISBN: 978-3-642-13232-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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