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Abstract — in this paper, we study the structure of WiMAX mesh networks and the influence of tree’s structure on the performance of the network. From a given network’s graph, we search for trees, which fulfill some network, QoS requirements. Since the searching space is very huge, we use genetic algorithm in order to find solution in acceptable time. We use NetKey representation which is an unbiased representation with high locality, and due to high locality we expect standard genetic operators like n-point cross over and mutation work properly and there is no need for problem specific operators. This encoding belongs to class of weighted encoding family. In contrast to other representation such as characteristics vector encoding which can only indicate whether a link is established or not, weighted encodings use weights for genotype and can thus encode the importance of links. Moreover, by using proper fitness function we can search for any desired QOS constraint in the network.
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Abstract — in this paper, we study the structure of WiMAX mesh networks and the influence of tree’s structure on the performance of the network. From a given network’s graph, we search for trees, which fulfill some network, QoS requirements. Since the searching space is very huge, we use genetic algorithm in order to find solution in acceptable time. We use NetKey representation which is an unbiased representation with high locality, and due to high locality we expect standard genetic operators like n-point cross over and mutation work properly and there is no need for problem specific operators. This encoding belongs to class of weighted encoding family. In contrast to other representation such as characteristics vector encoding which can only indicate whether a link is established or not, weighted encodings use weights for genotype and can thus encode the importance of links. Moreover, by using proper fitness function we can search for any desired QOS constraint in the network.
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