Traffic Engineering Next Generation IP Networks Using Gene Expression Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{bagula_2006_NOMS,
  author =       "Antoine B. Bagula",
  title =        "Traffic Engineering Next Generation IP Networks Using
                 Gene Expression Programming",
  booktitle =    "10th IEEE/IFIP Network Operations and Management
                 Symposium, NOMS 2006",
  year =         "2006",
  pages =        "230--239",
  address =      "Vancouver",
  organisation = "IFIP",
  publisher =    "IEEE",
  keywords =     "genetic algorithms, genetic programming, Gene
                 Expression Programming",
  DOI =          "doi:10.1109/NOMS.2006.1687554",
  size =         "10 pages",
  abstract =     "This paper addresses the problem of Traffic
                 Engineering (TE) to evaluate the performance of
                 evolutionary algorithms when used as IP routing
                 optimisers and assess the relevance of using {"}Gene
                 Expression Programming (GEP){"} as a new fine-tuning
                 algorithm in destination- and flow-based TE. We
                 consider a TE scheme where link weights are computed
                 using GEP and used as either fine-tuning parameters in
                 Open Shortest Path First (OSPF) routing or static
                 routing cost in Constraint Based Rouiigg((CRR. Thh
                 reeuutligg SPFa nd CBR algorithms are referred to as
                 OSPFgepand CBRgep. The GEP algorithm is based on a
                 hybrid optimisation model where local search
                 complements the global search implemented by classical
                 evolutionary algorithms to improve the genetic
                 individuals fitness through hill-climbing. We apply the
                 newly proposed TE scheme to compute the routing paths
                 for the traffic offered to a 23-, 28- and 30-node test
                 networks under different traffic conditions and
                 differentiated services situations. We evaluate the
                 performance achieved by the OSPFgep, CBRgepalgorithms
                 and OSPFmal, a destination-based routing algorithm
                 where OSPF path selection is driven by the link weights
                 computed by a Memetic Algorithm (MA). We compare the
                 performance achieved by the OSPFgepalgorithm to the
                 performance of the OSPFmaand OSPF algorithms in a
                 simulated routing environment using NS. We also compare
                 the quality of the paths found by the CBRgepalgorithm
                 to the quality of the paths computed by the Constraint
                 Shortest Path First (CSPF) algorithm when routing
                 bandwidth-guaranteed tunnels using connection-level
                 simulation.",
}

Genetic Programming entries for Antoine B Bagula

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