Exploring tradeoffs in pleiotropy and redundancy using evolutionary computing

Matthew J. Berryman; Wei-Li Khoo; Hiep Nguyen; Erin O'Neill; Andrew G. Allison; Derek Abbott

doi:10.1117/12.548001

29 March 2004 Exploring tradeoffs in pleiotropy and redundancy using evolutionary computing

Matthew J. Berryman, Wei-Li Khoo, Hiep Nguyen, Erin O'Neill, Andrew G. Allison, Derek Abbott

Proceedings Volume 5275, BioMEMS and Nanotechnology; (2004) https://doi.org/10.1117/12.548001
Event: Microelectronics, MEMS, and Nanotechnology, 2003, Perth, Australia

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.

Citation Download Citation

Matthew J. Berryman, Wei-Li Khoo, Hiep Nguyen, Erin O'Neill, Andrew G. Allison, and Derek Abbott "Exploring tradeoffs in pleiotropy and redundancy using evolutionary computing", Proc. SPIE 5275, BioMEMS and Nanotechnology, (29 March 2004); https://doi.org/10.1117/12.548001

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