Work

Parallelizing Gene Expression Programming Algorithm in Enabling Large-Scale Classification

Year: 2017

Type: article

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.

Source: Scientific Programming

DOI: 10.1155/2017/5081526

Authors: Lixiong Xu, Yuan Huang, Xiaodong Shen, Yang Liu

Institution: Sichuan University

Language: English

Topic: Evolutionary Algorithms and Applications

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,000

Funder: National Natural Science Foundation of China