Paper
16 January 2006 Multi-objective evolutionary algorithm for investigating the trade-off between pleiotropy and redundancy
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Proceedings Volume 6039, Complex Systems; 60390Q (2006) https://doi.org/10.1117/12.638406
Event: Microelectronics, MEMS, and Nanotechnology, 2005, Brisbane, Australia
Abstract
The trade-off between pleiotropy and redundancy in telecommunications networks is analyzed in this paper. They are optimized to reduce installation costs and propagation delays. Pleiotropy of a server in a telecommunications network is defined as the number of clients and servers that it can service whilst redundancy is described as the number of servers servicing a client. Telecommunications networks containing many servers with large pleiotropy are cost-effective but vulnerable to network failures and attacks. Conversely, those networks containing many servers with high redundancy are reliable but costly. Several key issues regarding the choice of cost functions and techniques in evolutionary computation (such as the modeling of Darwinian evolution, and mutualism and commensalism) will be discussed, and a future research agenda is outlined. Experimental results indicate that the pleiotropy of servers in the optimum network does improve, whilst the redundancy of clients do not vary significantly, as expected, with evolving networks. This is due to the controlled evolution of networks that is modeled by the steady-state genetic algorithm; changes in telecommunications networks that occur drastically over a very short period of time are rare.
© (2006) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Zhiyang Ong, Hao-Wei Lo, Matthew Berryman, and Derek Abbott "Multi-objective evolutionary algorithm for investigating the trade-off between pleiotropy and redundancy", Proc. SPIE 6039, Complex Systems, 60390Q (16 January 2006); https://doi.org/10.1117/12.638406
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KEYWORDS
Networks

Evolutionary algorithms

Telecommunications

Data modeling

Optimization (mathematics)

Systems modeling

Genetics

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