Parallelizing Gene Expression Programming Algorithm in Enabling Large-Scale Classification

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@Article{journals/sp/XuHSL17,

• author = "Lixiong Xu and Yuan Huang and Xiaodong Shen and Yang Liu2",
• title = "Parallelizing Gene Expression Programming Algorithm in Enabling Large-Scale Classification",
• journal = "Scientific Programming",
• year = "2017",
• volume = "2017",
• pages = "5081526:1--5081526:10",
• keywords = "genetic algorithms, genetic programming, gene expression programming",
• bibdate = "2017-05-28",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/sp/sp2017.html#XuHSL17",
• URL = "http://downloads.hindawi.com/journals/sp/2017/5081526.pdf",
• DOI = "doi:10.1155/2017/5081526",
• abstract = "As one of the most effective function mining algorithms, Gene Expression Programming (GEP) algorithm has been widely used in classification, pattern recognition, prediction, and other research fields. Based on the self-evolution, GEP is able to mine an optimal function for dealing with further complicated tasks. However, in big data researches, GEP encounters low efficiency issue due to its long time mining processes. To improve the efficiency of GEP in big data researches especially for processing large-scale classification tasks, this paper presents a parallelized GEP algorithm using MapReduce computing model. The experimental results show that the presented algorithm is scalable and efficient for processing large-scale classification tasks.",
• notes = "The Iris Dataset, The Wine Dataset",

}

Genetic Programming entries for Lixiong Xu Yuan Huang Xiaodong Shen Yang Liu2

Citations