Extremal Optimisation and Bin Packing

Authors:
Tim Hendtlass;Marcus Randall
Affiliations:
Faculty of Information and Communication Technologies, Swinburne University of Technology, Victoria, Australia;School of Information Technology, Bond University, Queensland, Australia
Venue:
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
Year:
2008

Citing 1
Cited 1

Enhancements to extremal optimisation for generalised assignment

ACAL'07 Proceedings of the 3rd Australian conference on Progress in artificial life

A Hybrid Extremal Optimisation Approach for the Bin Packing Problem

ACAL '09 Proceedings of the 4th Australian Conference on Artificial Life: Borrowing from Biology

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Abstract

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.