Pipelining Memetic Algorithms, Constraint Satisfaction, and Local Search for Course Timetabling

  • Authors:

  • Santiago E. Conant-Pablos;Dulce J. Magaña-Lozano;Hugo Terashima-Marín

  • Affiliations:

  • Centro de Computación Inteligente y Robótica, Tecnológico de Monterrey, Campus Monterrey, Monterrey, Mexico 64849;Centro de Computación Inteligente y Robótica, Tecnológico de Monterrey, Campus Monterrey, Monterrey, Mexico 64849;Centro de Computación Inteligente y Robótica, Tecnológico de Monterrey, Campus Monterrey, Monterrey, Mexico 64849

  • Venue:
  • MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
  • Year:
  • 2009

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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.