Genetic algorithms and call admission to telecommunications networks
Computers and Operations Research
On the distribution of rank of a random matrix over a finite field
Proceedings of the ninth international conference on on Random structures and algorithms
Uniform Crossover in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
An algebraic approach to network coding
IEEE/ACM Transactions on Networking (TON)
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Generating connected acyclic digraphs uniformly at random
Information Processing Letters
The encoding complexity of network coding
IEEE/ACM Transactions on Networking (TON) - Special issue on networking and information theory
A doubly distributed genetic algorithm for network coding
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A random key based genetic algorithm for the resource constrained project scheduling problem
Computers and Operations Research
Genetic Representations for Evolutionary Minimization of Network Coding Resources
Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog: Applications of Evolutionary Computing
An effective genetic algorithm for the network coding problem
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
IEEE Transactions on Information Theory
Information flow decomposition for network coding
IEEE Transactions on Information Theory
A Random Linear Network Coding Approach to Multicast
IEEE Transactions on Information Theory
Energy model of SARA and its performance analysis
WISM'12 Proceedings of the 2012 international conference on Web Information Systems and Mining
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The network coding problem (NCP), which aims to minimize network coding resources such as nodes and links, is a relatively new application of genetic algorithms (GAs) and hence little work has so far been reported in this area. Most of the existing literature on NCP has concentrated primarily on the static network coding problem (SNCP). There is a common assumption in work to date that a target rate is always achievable at every sink as long as coding is allowed at all nodes. In most real-world networks, such as wireless networks, any link could be disconnected at any time. This implies that every time a change occurs in the network topology, a new target rate must be determined. The SNCP software implementation then has to be re-run to try to optimize the coding based on the new target rate. In contrast, the GA proposed in this paper is designed with the dynamic network coding problem (DNCP) as the major concern. To this end, a more general formulation of the NCP is described. The new NCP model considers not only the minimization of network coding resources but also the maximization of the rate actually achieved at sinks. This is particularly important to the DNCP, where the target rate may become unachievable due to network topology changes. Based on the new NCP model, an effective GA is designed by integrating selected new problem-specific heuristic rules into the evolutionary process in order to better diversify chromosomes. In dynamic environments, the new GA does not need to recalculate target rate and also exhibits some degree of robustness against network topology changes. Comparative experiments on both SNCP and DNCP illustrate the effectiveness of our new model and algorithm.