An extended extremal optimisation model for parallel architectures

Marcus Randall*, Andrew Lewis

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-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 languageEnglish
Title of host publicatione-Science 2006 - Second IEEE International Conference on e-Science and Grid Computing
DOIs
Publication statusPublished - 2006
EventIEEE International Conference on e-Science and Grid Computing - Amsterdam, Netherlands
Duration: 4 Dec 20066 Dec 2006
Conference number: 2nd

Conference

ConferenceIEEE International Conference on e-Science and Grid Computing
Abbreviated titlee-Science 06
CountryNetherlands
CityAmsterdam
Period4/12/066/12/06

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    Randall, M., & Lewis, A. (2006). An extended extremal optimisation model for parallel architectures. In e-Science 2006 - Second IEEE International Conference on e-Science and Grid Computing [4031087] https://doi.org/10.1109/E-SCIENCE.2006.261198