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
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