An extended extremal optimisation model for parallel architectures

Marcus Randall; Andrew Lewis

doi:10.1109/E-SCIENCE.2006.261198

An extended extremal optimisation model for parallel architectures

Marcus Randall^*, Andrew Lewis

^*Corresponding author for this work

Bond Business School

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

13 Citations (Scopus)

Abstract

A relatively new meta-heuristic, known as extremal optimisation (EO), is based on the evolutionary science notion that poorly performing genes of an individual are replaced by random mutation over time. In combinatorial optimisation, the genes correspond to solution components. Using a generalised model of a parallel architecture, the EO model can readily be extended to a number of individuals using evolutionary population dynamics and concepts of self-organising criticality. These solutions are treated in a manner consistent with the EO model. That is, poorly performing solutions can be replaced by random ones. The performance of standard EO and the new system shows that it is capable of finding near optimal solutions efficiently to most of the test problems.

Original language	English
Title of host publication	e-Science 2006 - Second IEEE International Conference on e-Science and Grid Computing
DOIs	https://doi.org/10.1109/E-SCIENCE.2006.261198
Publication status	Published - 2006
Event	IEEE International Conference on e-Science and Grid Computing - Amsterdam, Netherlands Duration: 4 Dec 2006 → 6 Dec 2006 Conference number: 2nd

Conference

Conference	IEEE International Conference on e-Science and Grid Computing
Abbreviated title	e-Science 06
Country/Territory	Netherlands
City	Amsterdam
Period	4/12/06 → 6/12/06

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10.1109/E-SCIENCE.2006.261198

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AB - A relatively new meta-heuristic, known as extremal optimisation (EO), is based on the evolutionary science notion that poorly performing genes of an individual are replaced by random mutation over time. In combinatorial optimisation, the genes correspond to solution components. Using a generalised model of a parallel architecture, the EO model can readily be extended to a number of individuals using evolutionary population dynamics and concepts of self-organising criticality. These solutions are treated in a manner consistent with the EO model. That is, poorly performing solutions can be replaced by random ones. The performance of standard EO and the new system shows that it is capable of finding near optimal solutions efficiently to most of the test problems.

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Y2 - 4 December 2006 through 6 December 2006

ER -

An extended extremal optimisation model for parallel architectures

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