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 Ai-Hua Zhou

Citations