2007/02/07 by Minkyu Kim, Kim, Minkyu, Varun Aggarwal +7
Computer Science · #FOS: Computer and information sciences #Networking and Internet Architecture (cs.NI) #Neural and Evolutionary Computing (cs.NE) #cs.NE #cs.NI
paper · pdf · doi:10.48550/arxiv.cs/0702038
10 pages, 3 figures, accepted to the 4th European Workshop on the Application of Nature-Inspired Techniques to Telecommunication Networks and Other Connected Systems (EvoCOMNET 2007)
arxiv created 2007/02/07 · arxiv updated 2009/12/01
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.