Title: High performance parallel evolutionary algorithm model based on MapReduce framework

Authors: Xin Du; Youcong Ni; Zhiqiang Yao; Ruliang Xiao; Datong Xie

Addresses: 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

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.

Keywords: parallel evolutionary algorithms; MapReduce; large populations; large datasets; scalability; efficiency; parallel gene expression programming; symbolic regression.

DOI: 10.1504/IJCAT.2013.052807

International Journal of Computer Applications in Technology, 2013 Vol.46 No.3, pp.290 - 295

Received: 08 May 2021
Accepted: 12 May 2021

Published online: 23 Mar 2013 *

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