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- W2257053776 abstract "Computer networks are vital in our everyday lives. It is important to design network configurations with special consideration for their various aspects, such as security, integrity, scalability, and cost. It is especially important for a network to be built as robustly as possible to protect against failures, attacks, and intrusions. Through this work, I develop a novel method to assess and improve the robustness and efficiency of computer networks. This method uses computer network analysis, social network analysis, evolutionary computing, statistical methods, and graph theory. Specifically, my aim is to achieve enhanced network robustness and efficiency with a primary focus on architecture and topology of networks. I develop metrics for measuring the robustness and efficiency elements of networks and construct an evolutionary algorithm for the enhancement of these elements. I then apply the method to various networks, including random networks, biased networks, and real-life networks. I analyze these networks and enhance them using the evolutionary algorithm. Then, through using the metrics, I show how the robustness and efficiency of the networks improved. I further show how certain network parameters, as well as the network topological configuration, converge through this evolutionary process." @default.
- W2257053776 created "2016-06-24" @default.
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- W2257053776 date "2009-01-01" @default.
- W2257053776 modified "2023-09-26" @default.
- W2257053776 title "Enhancement of network robustness and efficiency through evolutionary computing, statistical computation and social network analysis" @default.
- W2257053776 hasPublicationYear "2009" @default.
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