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. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Kim, M., Aggarwal, V., O’Reilly, U. M., Médard, M., & Kim, W. (2007). Genetic representations for evolutionary minimization of network coding resources. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4448 LNCS, pp. 21–31). Springer Verlag. https://doi.org/10.1007/978-3-540-71805-5_3
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