Skip to main content
SpringerLink
Log in

Evolving timetabling heuristics using a grammar-based genetic programming hyper-heuristic framework

  • Special Issue - Regular Research Paper
  • Published:
Memetic Computing Aims and scope Submit manuscript

Abstract

This paper introduces a Grammar-based Genetic Programming Hyper-Heuristic framework (GPHH) for evolving constructive heuristics for timetabling. In this application GP is used as an online learning method which evolves heuristics while solving the problem. In other words, the system keeps on evolving heuristics for a problem instance until a good solution is found. The framework is tested on some of the most widely used benchmarks in the field of exam timetabling and compared with the best state-of-the-art approaches. Results show that the framework is very competitive with other constructive techniques, and did outperform other hyper-heuristic frameworks on many occasions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Abbass HA (2001) MBO: marriage in honey bees optimization—a haplometrosis polygynous swarming approach. In: Proceedings of the 2001 IEEE congress on evolutionary computation. IEEE Press, Seoul, Korea, pp 207–214

  2. Abdullah S, Ahmadi S, Burke EK, Dror M (2007) Investigating ahuja-borlins large neighbourhood search approach for examination timetabling. OR Spectr 29(2): 351–372

    Article  MATH  Google Scholar 

  3. Ahmadi S, Barrone R, Chen P, Cowling PI, McCollum B (2003) Perturbation based variable neighbourhood search in heuristic space for examination timetabling problem. In: Selected papers from MISTA 2003, pp 155–171

  4. Asmuni H, Burke EK, Garibaldi JM, McCollum B (2004) Fuzzy multiple heuristic orderings for examination timetabling. In: PATAT’04: The 4th international conference for the practice and pheory of automated timetabling, pp 334–353

  5. Bader-El-Den MB, Poli R (2007) Generating sat local-search heuristics using a gp hyper-heuristic framework. In: Monmarché N et al (eds) Artificial evolution, vol 4926, pp 37–49

  6. Bilgin B, Özcan E, Korkmaz EE (2006) An experimental study on hyper-heuristics and exam timetabling. In: PATAT’06: The 6th international conference for the practice and theory of automated timetabling, pp 394–412

  7. Brélaz D (1979) New methods to color the vertices of a graph. Commun ACM 22(4): 251–256

    Article  MATH  Google Scholar 

  8. Broder S (1964) Final examination scheduling. Commun ACM 7(8): 494–498

    Article  MATH  Google Scholar 

  9. Burke EK, Bykov Y, Newall J, Petrovic S (2004) A time-predefined local search approach to exam timetabling problems. IIE Trans 36(6): 509–528

    Article  Google Scholar 

  10. Burke EK, Dror M, Petrovic S, Qu R (2005) Hybrid graph heuristics in hyper-heuristics applied to exam timetabling problems. In: Golden BL et al (eds) The next wave in computing, optimization, and decision technologies. Springer, Maryland, pp 79–91

    Chapter  Google Scholar 

  11. Burke EK, Hyde MR, Kendall G (2006) Evolving bin packing heuristics with genetic programming. In: Runarsson et al (eds) Parallel problem solving from nature—PPSN IX, LNCS, vol 4193. Springer, Reykjavik, pp 860–869

    Chapter  Google Scholar 

  12. Burke EK, Hyde MR, Kendall G, Woodward J (2007) Automatic heuristic generation with genetic programming: evolving a jack-of-all-trades or a master of one. In: GECCO ’07: Proceedings of the 9th annual conference on genetic and evolutionary computation. ACM, New York, pp 1559–1565

  13. Burke EK, Kendall G, Silva DL, O’Brien R, Soubeiga E (2005) An ant algorithm hyperheuristic for the project presentation scheduling problem. In: 2005 IEEE congress on evolutionary computation. IEEE press, pp 2263–2270

  14. Burke EK, Kendall G, Soubeiga E (2003) A tabu-search hyperheuristic for timetabling and rostering. J Heuristics 9(6): 451–470

    Article  Google Scholar 

  15. Burke EK, McCollum B, Meisels A, Petrovic S, Qu R (2007) A graph-based hyper-heuristic for educational timetabling problems. Eur J Oper Res 176(1): 177–192

    Article  MATH  MathSciNet  Google Scholar 

  16. Burke EK, Newall JP (2002) Enhancing timetable solutions with local search methods. In: PATAT’02: The 3rd international conference for the practice and theory of automated timetabling, pp 195–206

  17. Burke EK, Petrovic S, Qu R (2006) Case-based heuristic selection for timetabling problems. J Sched 9(2): 115–132

    Article  MATH  Google Scholar 

  18. Burke KE, Kendall G, Newall J, Hart E, Ross P, Schulenburg S (2003) Hyper-heuristics: an emerging direction in modern search technology. In: Glover et al (eds) Handbook of metaheuristics. Kluwer, Dordrecht, pp 457–474

    Google Scholar 

  19. Caramia M, Dell’Olmo P, Italiano GF (2001) New algorithms for examination timetabling. In: WAE ’00: Proceedings of the 4th International workshop on algorithm engineering. Springer, London, pp 230–242

  20. Carter MW, Laporte G, Lee SY (1996) Examination timetabling: algorithmic strategies and. J Oper Res Soc 47: 73–83

    Article  Google Scholar 

  21. Casey S, Thompson J (2002) Grasping the examination scheduling problem. In: PATAT’02: The 4th international conference for the practice and theory of automated timetabling, pp 232–246

  22. Corne D, Ross P, lan Fang H (1994) Evolutionary timetabling: practice, prospects and work in progress. In: Proceedings of the UK planning and scheduling SIG workshop

  23. Corr PH, McCollum B, McGreevy MAJ, McMullan PJP (2006) A new neural network based construction heuristic for the examination timetabling problem. In: Procedings of 9th international conference parallel problem solving from nature—PPSN IX, pp 392–401

  24. Côté P, Wong T, Sabourin R (2004) A hybrid multi-objective evolutionary algorithm for the uncapacitated exam proximity problem. In: PATAT’04: The 4th international conference for the practice and theory of automated timetabling, pp 294–312

  25. Cowling PI, Kendall G, Han L (2002) An investigation of a hyperheuristic genetic algorithm applied to a trainer scheduling problem. In: Proceedings of the 2002 IEEE congress on evolutionary computation. IEEE press, Washington, DC, pp 1185–1190

  26. Di Gaspero L, Schaerf A (2003) Easylocal++: an object-oriented framework for the flexible design of local-search algorithms. Softw Pract Exp 33(8): 733–765

    Article  Google Scholar 

  27. Du J, Korkmaz E, Alhajj R, Barker K (2004) Novel clustering approach that employs genetic algorithm with new representation scheme and multiple objectives. In: DaWaK’04: Proceedings of the 6th international conference on data warehousing and knowledge discovery, vol 3181/2004. Springer, Heidelberg

  28. Erben W (2001) A grouping genetic algorithm for graph colouring and exam timetabling. In: PATAT ’00: The 3rd international conference for the practice and theory of automated timetabling. Springer, London, pp 132–158

  29. Ersoy E, Ozcan E, Etaner-Uyar AS (2007) Memetic algorithms and hyperhill-climbers. In: Baptiste J et al (eds) MISTA’07: The 3rd multidisciplinary international scheduling conference: theory and applications, pp 159–166

  30. Fukunaga AS (2002) Automated discovery of composite sat variable-selection heuristics. In: Proceedings of the18th national conference on artificial intelligence. AAAI, pp 641–648

  31. Gaspero LD, Schaerf A (2000) Tabu search techniques for examination timetabling. In: PATAT’06: The 3rd international conference for the practice and theory of automated timetabling, pp 104–117

  32. Gutin G, Karapetyan D (2009) A selection of useful theoretical tools for the design and analysis of optimization heuristics. Memet Comput 1(1): 25–34

    Article  Google Scholar 

  33. Kendall G, Hussin NM (2004) An investigation of a tabu search based hyper-heuristic for examination timetabling. In: Kendall G, Burke EK, Petrovic S (eds) Selected papers from MISTA 2003. Kluwer, Dordrecht

    Google Scholar 

  34. Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. MIT Press, Cambridge

    MATH  Google Scholar 

  35. Krasnogor N (2002) Studies on the theory and design space of memetic algorithms. Ph.D. thesis, University of the West of England

  36. Krasnogor N, Gustafson S (2004) A study on the use of “self- generation” in memetic algorithms. Nat Comput 3(1): 53–76

    Article  MATH  MathSciNet  Google Scholar 

  37. Merlot LTG, Boland N, Hughes BD, Stuckey PJ (2002) A hybrid algorithm for the examination timetabling problem. In: PATAT’02: The 3rd international conference for the practice and theory of automated timetabling, pp 207–231

  38. Montana DJ (1995) Strongly typed genetic programming. Evol Comput 3(2): 199–230

    Article  Google Scholar 

  39. Moscato P (1989) On evolution, search, optimization, genetic algorithms and martial arts: towards memetic algorithms. Technical Report 826, Caltech concurrent computation program

  40. Nguyen QH, Ong YS, Lim MH (2008) Non-genetic transmission of memes by diffusion. In: GECCO’08: Proceedings of the 10th annual conference on Genetic and evolutionary computation. ACM, New York, pp 1017–1024

  41. Ong YS, Kean AJ (2004) Meta-lamarckian learning in memetic algorithms. IEEE Trans Evol Comput 8(2): 99–110

    Article  Google Scholar 

  42. Ong YS, Lim MH, Zhu N, Wong KW (2006) Classification of adaptive memetic algorithms: a comparative study. IEEE Trans Syst Man Cybern B 36(1): 141–152

    Article  Google Scholar 

  43. Pappa G, Freitas A (2009) Evolving rule induction algorithms with multi-objective grammar-based genetic programming. Knowl Inf Syst 19(3): 283–309

    Article  Google Scholar 

  44. Poli R, Graff M (2009) There is a free lunch for hyper-heuristics, genetic programming and computer scientists. In: Vanneschi L et al (eds) EuroGP’09: Proceedings of the 12th european conference on genetic programming, vol 5481. Springer, pp 195–207

  45. Poli R, Langdon WB, McPhee NF (2008) A field guide to genetic programming. Published via http://lulu.com and freely available at http://www.gp-field-guide.org.uk

  46. Poli R, Woodward J, Burke EK (2007) A histogram-matching approach to the evolution of bin-packing strategies. In: 2007 IEEE congress on evolutionary computation. IEEE Press, Singapore, pp 3500–3507

  47. Ross P, Corne D, Terashima-Marín H (1996) The phase-transition niche for evolutionary algorithms in timetabling. In: PATAT’95: The 1st international conference for the practice and theory of automated timetabling. Springer, London, pp 309–324

  48. Ross P, Hart E, Corne D (1998) Some observations about ga-based exam timetabling. In: PATAT’97: The 2nd international conference for the practice and theory of automated timetabling. Springer, London, pp 115–129

  49. Ross P, Hart E, Corne D (2003) Genetic algorithms and timetabling. Springer, New York, pp 755–771

    Google Scholar 

  50. Schumacher C, Vose MD, Whitley LD (2001) The no free lunch and problem description length. In: Spector L et al (eds) GECCO’01: Proceedings of the 3rd genetic and evolutionary computation conference. Morgan Kaufmann, CA, USA, pp 565–570

    Google Scholar 

  51. Smith JE (2007) Coevolving memetic algorithms: a review and progress report. IEEE Trans Syst Man Cybern B 37(1): 6–17

    Article  Google Scholar 

  52. Terashima-Marin H, Ross P, Valenzuela-Rendon M (1999) Evolution of constraint satisfaction strategies in examination timetabling. In: Banzhaf W et al (eds) Proceedings of the genetic and evolutionary computation conference, vol 1. Morgan Kaufmann, Florida, pp 635–642

    Google Scholar 

  53. Ülker Ö, Özcan E, Korkmaz EE (2006) Linear linkage encoding in grouping problems: applications on graph coloring and timetabling. In: Burke EK, Rudová H (eds) PATAT’06: The 5th international conference for the practice and theory of automated timetabling, Lecture Notes in Computer Science, vol 3867. Springer, pp 347–363

  54. Voudouris C, Dorne R, Lesaint D, Liret A (2001) iopt: a software toolkit for heuristic search methods. In: Walsh T (ed) Proceedings of the 7th international conference on principles and practice of constraint programming, vol 2239. Springer, pp 716–719

  55. Welsh D, Powell M (1967) An upper bound for the chromatic number of a graph and its application to timetabling problems. Comput J 10(1): 85–87

    Article  MATH  Google Scholar 

  56. Whigham PA (1995) Grammatically-based genetic programming. In: Rosca JP (ed) Proceedings of the workshop on genetic programming: from theory to real-world applications. Tahoe City, CA, pp 33–41

  57. Whigham PA (1997) Evolving a program defined by a formal grammar. In: The 4th international conference on neural information processing—The annual conference of the Asian Pacific neural network assembly (ICONIP’97). Dunedin, New Zealand

  58. Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1): 67–82

    Article  Google Scholar 

  59. Zeleny M (1974) A concept of compromise solutions and the method of the displaced ideal. Comput Oper Res 1(3): 479–496

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Loughborough University, Loughborough, UK

    Mohamed Bader-El-Den & Shaheen Fatima

  2. School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK

    Riccardo Poli

Authors
  1. Mohamed Bader-El-Den
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Riccardo Poli
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shaheen Fatima
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohamed Bader-El-Den.

Additional information

The authors acknowledge financial support from EPSRC (grants EP/C523377/1 and EP/C523385/1).

Rights and permissions

Reprints and permissions

About this article

Cite this article

Bader-El-Den, M., Poli, R. & Fatima, S. Evolving timetabling heuristics using a grammar-based genetic programming hyper-heuristic framework. Memetic Comp. 1, 205–219 (2009). https://doi.org/10.1007/s12293-009-0022-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12293-009-0022-y

Keywords

Search

Navigation