Memory length in hyper-heuristics: An empirical study

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

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

13 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings of the 2007 IEEE Symposium on Computational Intelligence in Scheduling, CI-Sched 2007
Pages173-178
Number of pages6
DOIs
Publication statusPublished - 2007
Externally publishedYes
Event2007 IEEE Symposium on Computational Intelligence in Scheduling, CI-Sched 2007 - Honolulu, HI, United States
Duration: 1 Apr 20075 Apr 2007

Publication series

NameProceedings of the 2007 IEEE Symposium on Computational Intelligence in Scheduling, CI-Sched 2007

Conference

Conference2007 IEEE Symposium on Computational Intelligence in Scheduling, CI-Sched 2007
Country/TerritoryUnited States
CityHonolulu, HI
Period1/04/075/04/07

ASJC Scopus subject areas

  • Artificial Intelligence

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