Distributed Mining for Content Filtering Function Based on Simulated Annealing and Gene Expression Programming in Active Distribution Network

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@Article{journals/access/DengYYZ17,
  author =       "Song Deng and Changan Yuan and Jiquan Yang and 
                 Aihua Zhou",
  title =        "Distributed Mining for Content Filtering Function
                 Based on Simulated Annealing and Gene Expression
                 Programming in Active Distribution Network",
  journal =      "IEEE Access",
  year =         "2017",
  volume =       "5",
  pages =        "2319--2328",
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming",
  ISSN =         "2169-3536",
  bibdate =      "2017-05-27",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/journals/access/access5.html#DengYYZ17",
  URL =          "http://ieeexplore.ieee.org/document/7857022/",
  DOI =          "doi:10.1109/ACCESS.2017.2669106",
  size =         "10 pages",
  abstract =     "As an important part of the Internet of Energy, a
                 complex access environment, flexible access modes and a
                 massive number of access terminals, dynamic, and
                 distributed mass data in an active distribution network
                 will bring new challenges to the security of data
                 transmission. To address the emerging challenge of this
                 active distribution network, first we propose a content
                 filtering function mining algorithm based on simulated
                 annealing and gene expression programming (CFFM-SAGEP).
                 In CFFM-SAGEP, genetic operation based on simulated
                 annealing and dynamic population generation based on an
                 adaptive coefficient are applied to improve the
                 convergence speed and precision, the recall and the
                 Fbeta measure value of the content filtering. Finally,
                 based on CFFM-SAGEP, we present a distributed mining
                 for content filtering function based on simulated
                 annealing and gene expression programming (DMCF-SAGEP)
                 to improve efficiency of content filtering. In
                 DMCF-SAGEP, a local function merging strategy based on
                 the minimum residual sum of squares is designed to
                 obtain a global content filtering model. The results
                 using three data sets demonstrate that compared with
                 traditional algorithms, the algorithms proposed
                 demonstrate strong content filtering performance.",
}

Genetic Programming entries for Song Deng Chang-an Yuan Jiquan Yang Aihua Zhou

Citations