A Survey of Optimization by Building and Using Probabilistic Models
Computational Optimization and Applications
Multiple Adaptive Agents for Tactical Driving
Applied Intelligence
An algebraic approach to network coding
IEEE/ACM Transactions on Networking (TON)
A survey of combinatorial optimization problems in multicast routing
Computers and Operations Research
Soft Computing - A Fusion of Foundations, Methodologies and Applications
A Compact Genetic Algorithm with Elitism and Mutation Applied to Image Recognition
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
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
A population based incremental learning for delay constrained network coding resource minimization
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part II
IEEE Transactions on Evolutionary Computation
Elitism-based compact genetic algorithms
IEEE Transactions on Evolutionary Computation
A family of compact genetic algorithms for intrinsic evolvable hardware
IEEE Transactions on Evolutionary Computation
Population-Based Incremental Learning With Associative Memory for Dynamic Environments
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Information flow decomposition for network coding
IEEE Transactions on Information Theory
The encoding complexity of network coding
IEEE Transactions on Information Theory
Short Survey: A survey of application level multicast techniques
Computer Communications
Solving Japanese nonograms by Taguchi-based genetic algorithm
Applied Intelligence
EEM: evolutionary ensembles model for activity recognition in Smart Homes
Applied Intelligence
Information Sciences: an International Journal
A hierarchical parallel genetic approach for the graph coloring problem
Applied Intelligence
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In network coding based data transmission, intermediate nodes in the network are allowed to perform mathematical operations to recombine (code) data packets received from different incoming links. Such coding operations incur additional computational overhead and consume public resources such as buffering and computational resource within the network. Therefore, the amount of coding operations is expected to be minimized so that more public resources are left for other network applications.In this paper, we investigate the newly emerged problem of minimizing the amount of coding operations required in network coding based multicast. To this end, we develop the first elitism-based compact genetic algorithm (cGA) to the problem concerned, with three extensions to improve the algorithm performance. First, we make use of an all-one vector to guide the probability vector (PV) in cGA towards feasible individuals. Second, we embed a PV restart scheme into the cGA where the PV is reset to a previously recorded value when no improvement can be obtained within a given number of consecutive generations. Third, we design a problem-specific local search operator that improves each feasible solution obtained by the cGA. Experimental results demonstrate that all the adopted improvement schemes contribute to an enhanced performance of our cGA. In addition, the proposed cGA is superior to some existing evolutionary algorithms in terms of both exploration and exploitation simultaneously in reduced computational time.