Abstract
This chapter addresses the educational timetabling problem for multiple courses. This is a complex problem that basically involves a group of agents such as professors and lectures that must be weekly scheduled. The goal is to find solutions that satisfy the hard constraints and minimize the soft constraint violations. Moreover, universities often differ in terms of constraints and number of professors, courses, and resources involved, which increases the problem size and complexity. In this work, we propose a simple, scalable, and parameterized recursive approach to solve timetabling problems for multiple courses with genetic algorithms, which are efficient search methods used to achieve an optimal or near optimal solution.
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Alves, S.S.A., Oliveira, S.A.F., Rocha Neto, A.R. (2017). A Recursive Genetic Algorithm-Based Approach for Educational Timetabling Problems. In: Nedjah, N., Lopes, H., Mourelle, L. (eds) Designing with Computational Intelligence. Studies in Computational Intelligence, vol 664. Springer, Cham. https://doi.org/10.1007/978-3-319-44735-3_9
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