High performance parallel evolutionary algorithm model based on MapReduce framework
by Xin Du; Youcong Ni; Zhiqiang Yao; Ruliang Xiao; Datong Xie
International Journal of Computer Applications in Technology (IJCAT), Vol. 46, No. 3, 2013

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.

Online publication date: Wed, 29-May-2013

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