Route selection in backbone data communication networks
Computer Networks and ISDN Systems
Using genetic algorithms to improve pattern classification performance
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Routing and capacity assignment in backbone communication networks
Computers and Operations Research
Efficient scheduling of arbitrary task graphs to multiprocessors using a parallel genetic algorithm
Journal of Parallel and Distributed Computing - Special issue on parallel evolutionary computing
A genetic algorithm for designing distributed computer networktopologies
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Research: Routing in packet-switched communication networks
Computer Communications
A system for the design of packet-switched communication networks with economic tradeoffs
Computer Communications
An evolutionary algorithm that constructs recurrent neural networks
IEEE Transactions on Neural Networks
Allowing privacy protection algorithms to jump out of local optimums: an ordered greed framework
PinKDD'07 Proceedings of the 1st ACM SIGKDD international conference on Privacy, security, and trust in KDD
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In large-scale computer communication networks (e.g. the nowadays Internet), the assignment of link capacity and the selection of routes (or the assignment of flows) are extremely complex network optimization problems. Efficient solutions to these problems are much sought after because such solutions could lead to considerable monetary savings and better utilization of the networks. Unfortunately, as indicated by much prior theoretical research, these problems belong to the class of nonlinear combinatorial optimization problems, which are mostly (if not all) NP-hard problems. Although the traditional Lagrange relaxation and sub-gradient optimization methods can be used for tackling these problems, the results generated by these algorithms are locally optimal instead of globally optimal. In this paper, we propose a genetic algorithm based approach to providing optimized integrated solutions to the route selection and capacity flow assignment problems. With our novel formulation and genetic modeling, the proposed algorithm generates much better solutions than two well known efficient methods in our simulation studies.