Abstract
This paper introduces a hybrid algorithm that combines local search and constraint satisfaction techniques with memetic algorithms for solving Course Timetabling hard problems. These problems require assigning a set of courses to a predetermined finite number of classrooms and periods of time, complying with a complete set of hard constraints while maximizing the consistency with a set of preferences (soft constraints). The algorithm works in a three-stage sequence: first, it creates an initial population of approximations to the solution by partitioning the variables that represent the courses and solving each partition as a constraint-satisfaction problem; second, it reduces the number of remaining hard and soft constraint violations applying a memetic algorithm; and finally, it obtains a complete and fully consistent solution by locally searching around the best memetic solution. The approach produces competitive results, always getting feasible solutions with a reduced number of soft constraints inconsistencies, when compared against the methods running independently.
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Conant-Pablos, S.E., Magaña-Lozano, D.J., Terashima-Marín, H. (2009). Pipelining Memetic Algorithms, Constraint Satisfaction, and Local Search for Course Timetabling. In: Aguirre, A.H., Borja, R.M., Garciá, C.A.R. (eds) MICAI 2009: Advances in Artificial Intelligence. MICAI 2009. Lecture Notes in Computer Science(), vol 5845. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05258-3_36
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DOI: https://doi.org/10.1007/978-3-642-05258-3_36
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