A critical point for random graphs with a given degree sequence
Random Graphs 93 Proceedings of the sixth international seminar on Random graphs and probabilistic methods in combinatorics and computer science
On power-law relationships of the Internet topology
Proceedings of the conference on Applications, technologies, architectures, and protocols for computer communication
`` Direct Search'' Solution of Numerical and Statistical Problems
Journal of the ACM (JACM)
The Diameter of a Scale-Free Random Graph
Combinatorica
Local search genetic algorithm for optimal design of reliablenetworks
IEEE Transactions on Evolutionary Computation
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
IEEE Transactions on Evolutionary Computation
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We address the design of complex, large-scale systems by viewing them as random networks, and optimizing network structure over generative parameters. We do not seek specific topologies, but rather classes of optimal or near optimal networks which correspond to desirable statistical behavior, while also allowing flexibility to accommodate unmodeled constraints. This approach is a computationally feasible forward design path for large-scale systems. A numerical example is given in which a network's degree distribution is optimized for combined efficiency and robustness in a cascading failure scenario; the work has application to electric distribution and other systems.