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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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
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