Computer Science – Neural and Evolutionary Computing
Scientific paper
2004-04-07
Proc. SPIE 5275, BioMEMS and Nanotechnology, Ed. Dan V. Nicolau, Perth, Australia, Dec. 2003, pp49-58
Computer Science
Neural and Evolutionary Computing
10 pages, 6 figures
Scientific paper
10.1117/12.548001
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.
Abbott Derek
Allison Andrew
Berryman Matthew J.
Khoo Wei-Li
Nguyen Hoi H.
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