文章基本信息

标题：Parallelizing Gene Expression Programming Algorithm in Enabling Large-Scale Classification
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作者：Lixiong Xu ; Yuan Huang ; Xiaodong Shen 等
期刊名称：Scientific Programming
印刷版ISSN：1058-9244
出版年度：2017
卷号：2017
DOI：10.1155/2017/5081526
出版社：Hindawi Publishing Corporation
摘要：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.