Extremal optimisation and bin packing

Tim Hendtlass*, Marcus Randall

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

3 Citations (Scopus)


Extremal Optimisation (EO) is a fairly new entrant into the realms of stochastic based optimisation techniques. Its behaviour differs from other more common algorithms as it alters a poorly performing part of the one solution used without regard to the effect this will have on the quality of the solution. While this means that its performance on assignment problems may be poor if used on its own, this same 'failing' makes it a very suitable base for a meta-heuristic. An analysis of the performance of naive EO on the classic bin packing problem is performed in this paper. Results are also presented that show that the same naive EO can be used in a meta-heuristic that performs very well.

Original languageEnglish
Title of host publicationSimulated Evolution and Learning - 7th International Conference, SEAL 2008, Proceedings
Number of pages9
Volume5361 LNAI
Publication statusPublished - 2008
Event7th International Conference on Simulated Evolution and Learning, SEAL 2008 - Melbourne, VIC, Australia
Duration: 7 Dec 200810 Dec 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5361 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349


Conference7th International Conference on Simulated Evolution and Learning, SEAL 2008
CityMelbourne, VIC


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