Computer Science – Neural and Evolutionary Computing
Scientific paper
2007-02-07
Computer Science
Neural and Evolutionary Computing
10 pages, 3 figures, accepted to the 4th European Workshop on the Application of Nature-Inspired Techniques to Telecommunicati
Scientific paper
We demonstrate how a genetic algorithm solves the problem of minimizing the resources used for network coding, subject to a throughput constraint, in a multicast scenario. A genetic algorithm avoids the computational complexity that makes the problem NP-hard and, for our experiments, greatly improves on sub-optimal solutions of established methods. We compare two different genotype encodings, which tradeoff search space size with fitness landscape, as well as the associated genetic operators. Our finding favors a smaller encoding despite its fewer intermediate solutions and demonstrates the impact of the modularity enforced by genetic operators on the performance of the algorithm.
Aggarwal Varun
Kim Minkyu
Kim Wonsik
Medard Muriel
O'Reilly Una-May
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