High performance parallel evolutionary algorithm model based on MapReduce framework

Authors:
Xin Du;Youcong Ni;Zhiqiang Yao;Ruliang Xiao;Datong Xie
Affiliations:
Faculty of Software, Fujian Normal University, Fuzhou, Fujian, 350108, China;Faculty of Software, Fujian Normal University, Fuzhou, Fujian, 350108, China;Faculty of Software, Fujian Normal University, Fuzhou, Fujian, 350108, China;Faculty of Software, Fujian Normal University, Fuzhou, Fujian, 350108, China;Department of Information Management Engineering, Fujian Commercial College, Fuzhou, Fujian, 350012, China
Venue:
International Journal of Computer Applications in Technology
Year:
2013

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Abstract

Evolutionary algorithms EAs are increasingly being applied to large-scale problems. MapReduce is a powerful abstraction proposed by Google for making scalable and fault tolerant applications. However, how to design high performance parallel EA based on MapReduce MR-PEA is still an open problem. In this paper, a parallel evolutionary algorithm model based on MapReduce by improving traditional parallel evolutionary algorithms model is proposed. The MR-PEA model is fit for large populations and datasets, has the characteristic of high scalable and efficiency. In order to justify the effectiveness of the MR-PEA model, we proposed a parallel gene expression programming based on MapReduce MR-GEP used to solve symbolic regression.