• DocumentCode
    2709634
  • Title

    Memory Length in Hyper-heuristics: An Empirical Study

  • Author

    Bai, Ruibin ; Burke, Edmund K. ; Gendreau, Michel ; Kendall, Graham ; McCollum, Barry

  • Author_Institution
    Sch. of Comput. Sci. & IT, Nottingham Univ.
  • fYear
    2007
  • fDate
    1-5 April 2007
  • Firstpage
    173
  • Lastpage
    178
  • Abstract
    Hyper-heuristics are an emergent optimisation methodology which aims to give a higher level of flexibility and domain-independence than is currently possible. Hyper-heuristics are able to adapt to the different problems or problem instances by dynamically choosing between heuristics during the search. This paper is concerned with the issues of memory length on the performance of hyper-heuristics. We focus on a recently proposed simulated annealing hyper-heuristic and choose a set of hard university course timetabling problems as the test bed for this empirical study. The experimental results show that the memory length can affect the performance of hyper-heuristics and a good choice of memory length is able to improve solution quality. Finally, two dynamic approaches are investigated and one of the approaches is shown to be able to produce promising results without introducing extra sensitive algorithmic parameters.
  • Keywords
    educational courses; educational institutions; simulated annealing; hard university course timetabling problem; hyperheuristics; memory length; optimisation methodology; simulated annealing; Algorithm design and analysis; Computational intelligence; Machine learning; Machine learning algorithms; Optimization methods; Probability distribution; Processor scheduling; Simulated annealing; Switches; Testing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Intelligence in Scheduling, 2007. SCIS '07. IEEE Symposium on
  • Conference_Location
    Honolulu, HI
  • Print_ISBN
    1-4244-0704-4
  • Type

    conf

  • DOI
    10.1109/SCIS.2007.367686
  • Filename
    4218613