Performance Comparison of Evolutionary Algorithms for University Course Timetabling Problem

Autores/as

  • Noel Rodríguez Maya Instituto Tecnológico de Zitácuaro, Departamento de Sistemas y Computación, Michoacán
  • Juan J. Flores Universidad Michoacana de San Nicolas de Hidalgo, Departamento de Estudios de Posgrado de la Facultad de Ingeniería Eléctrica, Morelia, Michoacán
  • Hector Rodríguez Rangel Instituto Tecnológico de Culiacán, Departamento de Estudios de Posgrado e Investigación, Sinaloa

DOI:

https://doi.org/10.13053/cys-20-4-2504

Palabras clave:

University course timetabling problem, evolutionary algorithms, optimization, real life applications.

Resumen

In literature, University Course Timetabling Problem (UCTP) is a well known combinational problem. The main reasons to study this problem are the intrinsic importance at the interior of universities, the exponential number of solutions, and the distinct types of approaches to solve this problem. Due to the exponential number of solutions (combinations), this problem is categorized as NP-hard. Generally, Evolutionary Algorithms (EA) are efficient tools to solve this problem. Differential Evolution (DE) has been widely used to solve complex optimization problems on the continuous domain, Genetic Algorithms (GA) has been adopted to solve different types of problems and even as point of comparison between algorithms performance. This paper examines and compares the performance depicted by two approaches based on EA to solve the UCTP: the DE and the GA approaches. The experiments use a set of 3 real life UCTP instances, each instance contains different characteristics and are based on Mexican universities. In the experiments, we used the optimal input parameters for the solvers, and we performed a qualitative-quantitative comparison between the final solutions. The results showed the best performance for the solution based on the DE algorithm. This work can be easily extended to use other algorithms and UCTP instances.

Biografía del autor/a

Noel Rodríguez Maya, Instituto Tecnológico de Zitácuaro, Departamento de Sistemas y Computación, Michoacán

Is professor at Instituto Tecnológico de Zitácuaro since 2006. He got his B. Eng. degree in Computational Systems Engineering from the Instituto Tecnológico de Zitácuaro in 2004, in 2006 he got a M. Sc. In Computation from the Laboratorio Nacional de Informática Avanzada, and in 2016 he got a Ph.D. in Electrical Engineering from the Universidad Michoacana de San Nicolás de Hidalgo. His áreas of interest are Evolutionary Computation and Data Mining.

Juan J. Flores, Universidad Michoacana de San Nicolas de Hidalgo, Departamento de Estudios de Posgrado de la Facultad de Ingeniería Eléctrica, Morelia, Michoacán

Got a B.Sc. degree in Electrical Engineering from the Universidad Michoacana in 1984. In 1986 he got a M.Sc. degree in computer science from Centro de Investigación ´on y Estudios Avanzados, of the Instituto Politécnico Nacional. In 1997 got a Ph.D. degree in Computer Science from the University of Oregon, USA. He is a full time professor at the Universidad Michoacana since 1986. His research work deals with applications of Artificial Intelligence to Electrical Engineering and Financial Analysis. He is a member of the Sistema Nacional de Investigadores, author of several scientific articles in international conferences and journals. He is a member of the editorial committee of several journals and reviewer of journals and conferences; he is also a certified reviewer for Conacyt. He was an invited Professor-Researcher at the University of Oregon in 2005/2006 and 2012/2013.

Hector Rodríguez Rangel, Instituto Tecnológico de Culiacán, Departamento de Estudios de Posgrado e Investigación, Sinaloa

Got his B. Eng. degree in Computational Systems Engineering from Tecnológico de Morelia in 2006. In 2009 he got a M.Sc., and in 2014 he got his Ph.D. in both cases in Electrical Engineering at Universidad Michoacana. He perform a postdoctoral research at University Polytechnic of Catalunya in 2015. He is a full time profesor at Instituto Tecnológico de Culiacán since 2016. His research work deals with applications of Artificial Intelligence to Electrical Engineering, and time series forecasting.

Descargas

Publicado

2016-12-18

Número

Vol. 20 Núm. 4 (2016): Thematic Issue: Research Advances and Applications of Evolutionary Computation (Guest Editors: H.J. Escalante, M. Graff)

Sección

Articles of the Thematic Issue

Licencia

Transfiero exclusivamente a la revista “Computación y Sistemas”, editada por el Centro de Investigación en Computación (CIC), los Derechos de Autor del artículo antes mencionado, asimismo acepto que no serán transferidos a ninguna otra publicación, en cualquier formato, idioma, medio existente (incluyendo los electrónicos y multimedios) o por desarrollar.

Certifico que el artículo, no ha sido divulgado previamente o sometido simultáneamente a otra publicación y que no contiene materiales cuya publicación violaría los Derechos de Autor u otros derechos de propiedad de cualquier persona, empresa o institución. Certifico además que tengo autorización de la institución o empresa donde trabajo o estudio para publicar este Trabajo.

El autor, representante acepta la responsabilidad por la publicación del Trabajo en nombre de todos y cada uno de los autores.

Esta Transferencia está sujeta a las siguientes reservas:

  • Los autores conservan todos los derechos de propiedad (tales como derechos de patente) de este Trabajo, con excepción de los derechos de publicación transferidos al CIC, mediante este documento.
  • Los autores conservan el derecho de publicar el Trabajo total o parcialmente en cualquier libro del que ellos sean autores o editores y hacer uso personal de este trabajo en conferencias, cursos, páginas web personal, etc.