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Proceedings Paper

Exploring tradeoffs in pleiotropy and redundancy using evolutionary computing
Author(s): Matthew J. Berryman; Wei-Li Khoo; Hiep Nguyen; Erin O'Neill; Andrew G. Allison; Derek Abbott
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Paper Abstract

Evolutionary computation algorithms are increasingly being used to solve optimization problems as they have many advantages over traditional optimization algorithms. In this paper we use evolutionary computation to study the trade-off between pleiotropy and redundancy in a client-server based network. Pleiotropy is a term used to describe components that perform multiple tasks, while redundancy refers to multiple components performing one same task. Pleiotropy reduces cost but lacks robustness, while redundancy increases network reliability but is more costly, as together, pleiotropy and redundancy build flexibility and robustness into systems. Therefore it is desirable to have a network that contains a balance between pleiotropy and redundancy. We explore how factors such as link failure probability, repair rates, and the size of the network influence the design choices that we explore using genetic algorithms.

Paper Details

Date Published: 29 March 2004
PDF: 10 pages
Proc. SPIE 5275, BioMEMS and Nanotechnology, (29 March 2004); doi: 10.1117/12.548001
Show Author Affiliations
Matthew J. Berryman, The Univ. of Adelaide (Australia)
Wei-Li Khoo, The Univ. of Adelaide (Australia)
Hiep Nguyen, The Univ. of Adelaide (Australia)
Erin O'Neill, Univ. of Newcastle (Australia)
Andrew G. Allison, The Univ. of Adelaide (Australia)
Derek Abbott, The Univ. of Adelaide (Australia)

Published in SPIE Proceedings Vol. 5275:
BioMEMS and Nanotechnology
Dan V. Nicolau; Uwe R. Muller; John M. Dell, Editor(s)

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