Comparison of Bioinspired Algorithms Applied to the Timetabling Problem

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1257)


The problem of timetabling events is present in various organizations such as schools, hospitals, transportation centers. The purpose of timetabling activities at a university is to ensure that all students attend their required subjects in accordance with the available resources. The set of constraints that must be considered in the design of timetables involves students, teachers and infrastructure. This study shows that acceptable solutions are generated through the application of genetic, memetic and immune system algorithms for the problem of timetabling. The algorithms are applied to real instances of the University of Mumbai in India and their results are comparable with those of a human expert.


Genetic algorithm Memetic algorithm Immune system Faculty timetabling Course timetabling 


  1. 1.
    Jorge AS, Martin CJ, Hugo T (2010) Academic timetabling design using hyper—heuristics. Springer, Berlin, pp 43–56.
  2. 2.
    Asratian AS, de Werra D (2002) A generalized class–teacher model for some timetabling problems. University of Technology, Department of Engineering Sciences and Mathematics, Mathematical Science, & Mathematics. Eur J Oper Res 531–542.
  3. 3.
    Soria-Alcaraz Jorge A, Martín C, Héctor P, Sotelo-Figueroa MA 2013) Comparison of metaheuristic algorithms with a methodology of design for the evaluation of hard constraints over the course timetabling problem. Springer, Berlin, pp 289–302.
  4. 4.
    Viloria A, Lis-Gutiérrez JP, Gaitán-Angulo M, Godoy ARM, Moreno GC, Kamatkar SJ (2018) Methodology for the design of a student pattern recognition tool to facilitate the teaching—learning process through knowledge data discovery (big data). In: Tan Y, Shi Y, Tang Q (eds) Data mining and big data. DMBD 2018. Lecture notes in computer science, vol 10943. Springer, ChamGoogle Scholar
  5. 5.
    De Werra D (1985) An introduction to timetabling. Eur J Oper Res 19(2):151–162MathSciNetCrossRefGoogle Scholar
  6. 6.
    Obit JH, Ouelhadj D, Landa-Silva D, Vun TK, Alfred R (2011) Designing a multi-agent approach system for distributed course timetabling, pp 103–108.
  7. 7.
    Lewis MRR (2006) Metaheuristics for university course timetabling. Ph.D. Thesis, Napier UniversityGoogle Scholar
  8. 8.
    Deng X, Zhang Y, Kang B, Wu J, Sun X, Deng Y (2011) An application of genetic algorithm for university course timetabling problem, pp 2119–2122./
  9. 9.
    Mahiba AA, Durai CAD (2012) Genetic algorithm with search bank strategies for university course timetabling problem. Procedia Eng 38:253–263CrossRefGoogle Scholar
  10. 10.
    Kamatkar SJ, Kamble A, Viloria A, Hernández-Fernandez L, Cali EG (2018) Database performance tuning and query optimization. In: International conference on data mining and big data. Springer, Cham, pp 3–11Google Scholar
  11. 11.
    Nguyen K, Lu T, Le T, Tran N (2011) Memetic algorithm for a university course timetabling problem, pp. 67–71.
  12. 12.
    Aladag C, Hocaoglu G (2007) A tabu search algorithm to solve a course timetabling problem. Hacettepe J Math Stat, pp 53–64Google Scholar
  13. 13.
    Moscato P (1989) On evolution, search, optimization, genetic algorithms and martial arts: towards memetic algorithms. Caltech Concurrent Computation Program (report 826)Google Scholar
  14. 14.
    Frausto-Solís J, Alonso-Pecina F, Mora-Vargas J (2008) An efficient simulated annealing algorithm for feasible solutions of course timetabling. Springer, Berlin, pp 675–685Google Scholar
  15. 15.
    Joudaki M, Imani M, Mazhari N (2010) Using improved memetic algorithm and local search to solve university course timetabling problem (UCTTP). Islamic Azad University, DoroudGoogle Scholar
  16. 16.
    Thepphakorn T, Pongcharoen P, Hicks C (2014) An ant colony based timetabling tool. Int J Prod Econ 149:131–144. Scholar
  17. 17.
    Soria-Alcaraz J, Ochoa G, Swan J, Carpio M, Puga H, Burke E (2014) Effective learning hyper-heuristics for the course timetabling problem. Eur J Oper Res 77–86.
  18. 18.
    Wolpert H, Macready G (1996) No free lunch theorems for search. Technical report, The Santa Fe Institute, vol 1Google Scholar
  19. 19.
    Lai LF, Wu C, Hsueh N, Huang L, Hwang S (2008) An artificial intelligence approach to course timetabling. Int J Artif Intell Tools 223–240.
  20. 20.
    McCollum B, McMullan P, Parkes AJ, Burke EK, Qu R (2012) A new model for automated examination timetabling. Ann Oper Res 291–315Google Scholar
  21. 21.
    Conant-Pablos SE et al (2009) Pipelining memetic algorithms, constraint satisfaction, and local search for course timetabling. In: MICAI Mexican international conference on artificial intelligence, vol 1, pp 408–419Google Scholar
  22. 22.
    Carpio-Valadez JM (2006) Integral model for optimal assignation of academic tasks. In: Encuentro de investigacion en ingenieria electrica. ENVIE, Zacatecas, pp 78–83Google Scholar
  23. 23.
    Soria-Alcaraz JA, Martin C, Héctor P, Hugo T, Laura CR, Sotelo-Figueroa MA (2013) Methodology of design: a novel generic approach applied to the course timetabling problem, pp 287–319.
  24. 24.
    Talbi E (2009) Metaheuristics: from design to implementation. Wiley, USCrossRefGoogle Scholar
  25. 25.
    Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley Pub. Co, ReadingzbMATHGoogle Scholar
  26. 26.
    Yang X-S (2010) Nature-inspired metaheuristic algorithms. Luniver PressGoogle Scholar
  27. 27.
    Abdoun O, Abouchabaka J (2011) A comparative study of adaptive crossover operators for genetic algorithms to resolve the traveling salesman problem. Int J Comput ApplGoogle Scholar
  28. 28.
    Derrac J, García S (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence. In: Swarm and Evolutionary ComputationGoogle Scholar
  29. 29.
    Azuaje F (2003) Review of “Artificial immune systems: a new computational intelligence approach.” J Neural Netw 16(8):1229–1229Google Scholar
  30. 30.
    Maulik U, Bandyopadhyay S (2000) Genetic algorithm-based clustering technique. Pattern Recogn 33:1455–1465CrossRefGoogle Scholar
  31. 31.
    Lü Z, Hao J (2010) Adaptive tabu search for course timetabling. Eur J Oper Res 235–244.
  32. 32.
    Viloria A, Lezama OBP (2019) Improvements for determining the number of clusters in k-means for innovation databases in SMEs. Procedia Comput Sci 151:1201–1206CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2021

Authors and Affiliations

  1. 1.Universidad de Ciencias AplicadasLimaPeru
  2. 2.Universidad de la CostaBarranquillaColombia
  3. 3.Universidad TecnológicaSan Pedro SulaHonduras

Personalised recommendations