Investigating Fitness Measures for the Automatic Construction of Graph Models

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Kyle Harrison and Mario Ventresca and 
                 Beatrice Ombuki-Berman",
  title =        "Investigating Fitness Measures for the Automatic
                 Construction of Graph Models",
  booktitle =    "18th European Conference on the Applications of
                 Evolutionary Computation",
  year =         "2015",
  editor =       "Antonio M. Mora and Giovanni Squillero",
  series =       "LNCS",
  volume =       "9028",
  publisher =    "Springer",
  pages =        "189--200",
  address =      "Copenhagen",
  month =        "8-10 " # apr,
  organisation = "EvoStar",
  keywords =     "genetic algorithms, genetic programming, Complex
                 networks, Graph models, Centrality measures,
  isbn13 =       "978-3-319-16548-6",
  DOI =          "doi:10.1007/978-3-319-16549-3_16",
  abstract =     "Graph models are often constructed as a tool to better
                 understand the growth dynamics of complex networks.
                 Traditionally, graph models have been constructed
                 through a very time consuming and difficult manual
                 process. Recently, there have been various methods
                 proposed to alleviate the manual efforts required when
                 constructing these models, using statistical and
                 evolutionary strategies. A major difficulty associated
                 with automated approaches lies in the evaluation of
                 candidate models. To address this difficulty, this
                 paper examines a number of well-known network
                 properties using a proposed meta-analysis procedure.
                 The meta-analysis demonstrated how these network
                 measures interacted when used together as classifiers
                 to determine network, and thus model, (dis)similarity.
                 The analytical results formed the basis of a fitness
                 evaluation scheme used in a genetic programming (GP)
                 system to automatically construct graph models for
                 complex networks. The GP-based automatic inference
                 system was used to reproduce two well-known graph
                 models, the results of which indicated that the evolved
                 models exemplified striking similarity when compared to
                 their respective targets on a number of structural
                 network properties.",
  notes =        "EvoCOMPLEX EvoApplications2015 held in conjunction
                 with EuroGP'2015, EvoCOP2015 and EvoMusArt2015

Genetic Programming entries for Kyle Robert Harrison Mario Ventresca Beatrice Ombuki-Berman