Advertisement

Memetic Computing

, 1:205 | Cite as

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

  • Mohamed Bader-El-Den
  • Riccardo Poli
  • Shaheen Fatima
Special Issue - Regular Research Paper

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.

Keywords

Timetabling Genetic programming Hyper-heuristics Heuristics 

References

  1. 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 207–214Google Scholar
  2. 2.
    Abdullah S, Ahmadi S, Burke EK, Dror M (2007) Investigating ahuja-borlins large neighbourhood search approach for examination timetabling. OR Spectr 29(2): 351–372zbMATHCrossRefGoogle Scholar
  3. 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 155–171Google Scholar
  4. 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 334–353Google Scholar
  5. 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 37–49Google Scholar
  6. 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 394–412Google Scholar
  7. 7.
    Brélaz D (1979) New methods to color the vertices of a graph. Commun ACM 22(4): 251–256zbMATHCrossRefGoogle Scholar
  8. 8.
    Broder S (1964) Final examination scheduling. Commun ACM 7(8): 494–498zbMATHCrossRefGoogle Scholar
  9. 9.
    Burke EK, Bykov Y, Newall J, Petrovic S (2004) A time-predefined local search approach to exam timetabling problems. IIE Trans 36(6): 509–528CrossRefGoogle Scholar
  10. 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 79–91CrossRefGoogle Scholar
  11. 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 860–869CrossRefGoogle Scholar
  12. 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 1559–1565Google Scholar
  13. 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 2263–2270Google Scholar
  14. 14.
    Burke EK, Kendall G, Soubeiga E (2003) A tabu-search hyperheuristic for timetabling and rostering. J Heuristics 9(6): 451–470CrossRefGoogle Scholar
  15. 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): 177–192zbMATHCrossRefMathSciNetGoogle Scholar
  16. 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 195–206Google Scholar
  17. 17.
    Burke EK, Petrovic S, Qu R (2006) Case-based heuristic selection for timetabling problems. J Sched 9(2): 115–132zbMATHCrossRefGoogle Scholar
  18. 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 457–474Google Scholar
  19. 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 230–242Google Scholar
  20. 20.
    Carter MW, Laporte G, Lee SY (1996) Examination timetabling: algorithmic strategies and. J Oper Res Soc 47: 73–83CrossRefGoogle Scholar
  21. 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 232–246Google Scholar
  22. 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 workshopGoogle Scholar
  23. 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 392–401Google Scholar
  24. 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 294–312Google Scholar
  25. 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 1185–1190Google Scholar
  26. 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): 733–765CrossRefGoogle Scholar
  27. 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, HeidelbergGoogle Scholar
  28. 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 132–158Google Scholar
  29. 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 159–166Google Scholar
  30. 30.
    Fukunaga AS (2002) Automated discovery of composite sat variable-selection heuristics. In: Proceedings of the18th national conference on artificial intelligence. AAAI, pp 641–648Google Scholar
  31. 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 104–117Google Scholar
  32. 32.
    Gutin G, Karapetyan D (2009) A selection of useful theoretical tools for the design and analysis of optimization heuristics. Memet Comput 1(1): 25–34CrossRefGoogle Scholar
  33. 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, DordrechtGoogle Scholar
  34. 34.
    Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. MIT Press, CambridgezbMATHGoogle Scholar
  35. 35.
    Krasnogor N (2002) Studies on the theory and design space of memetic algorithms. Ph.D. thesis, University of the West of EnglandGoogle Scholar
  36. 36.
    Krasnogor N, Gustafson S (2004) A study on the use of “self- generation” in memetic algorithms. Nat Comput 3(1): 53–76zbMATHCrossRefMathSciNetGoogle Scholar
  37. 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 207–231Google Scholar
  38. 38.
    Montana DJ (1995) Strongly typed genetic programming. Evol Comput 3(2): 199–230CrossRefGoogle Scholar
  39. 39.
    Moscato P (1989) On evolution, search, optimization, genetic algorithms and martial arts: towards memetic algorithms. Technical Report 826, Caltech concurrent computation programGoogle Scholar
  40. 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 1017–1024Google Scholar
  41. 41.
    Ong YS, Kean AJ (2004) Meta-lamarckian learning in memetic algorithms. IEEE Trans Evol Comput 8(2): 99–110CrossRefGoogle Scholar
  42. 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): 141–152CrossRefGoogle Scholar
  43. 43.
    Pappa G, Freitas A (2009) Evolving rule induction algorithms with multi-objective grammar-based genetic programming. Knowl Inf Syst 19(3): 283–309CrossRefGoogle Scholar
  44. 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 195–207Google Scholar
  45. 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. 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 3500–3507Google Scholar
  47. 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 309–324Google Scholar
  48. 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 115–129Google Scholar
  49. 49.
    Ross P, Hart E, Corne D (2003) Genetic algorithms and timetabling. Springer, New York, pp 755–771Google Scholar
  50. 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 565–570Google Scholar
  51. 51.
    Smith JE (2007) Coevolving memetic algorithms: a review and progress report. IEEE Trans Syst Man Cybern B 37(1): 6–17CrossRefGoogle Scholar
  52. 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 635–642Google Scholar
  53. 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 347–363Google Scholar
  54. 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 716–719Google Scholar
  55. 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): 85–87zbMATHCrossRefGoogle Scholar
  56. 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 33–41Google Scholar
  57. 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 ZealandGoogle Scholar
  58. 58.
    Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1): 67–82CrossRefGoogle Scholar
  59. 59.
    Zeleny M (1974) A concept of compromise solutions and the method of the displaced ideal. Comput Oper Res 1(3): 479–496CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  • Mohamed Bader-El-Den
    • 1
  • Riccardo Poli
    • 2
  • Shaheen Fatima
    • 1
  1. 1.Department of Computer ScienceLoughborough UniversityLoughboroughUK
  2. 2.School of Computer Science and Electronic EngineeringUniversity of EssexColchesterUK

Personalised recommendations