Paper Info
Title
High performance parallel evolutionary algorithm model based on MapReduce framework
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.
Year
DOI
Venue
2013
10.1504/IJCAT.2013.052807
IJCAT
Keywords
Field
DocType
parallel gene expression programming,parallel evolutionary algorithms model,mapreduce mr-gep,mapreduce framework,fault tolerant application,mr-pea model,high performance,evolutionary algorithms eas,high scalable,mapreduce mr-pea,parallel evolutionary algorithm model,evolutionary algorithms
Gene expression programming,Open problem,Abstraction,Evolutionary algorithm,Computer science,Parallel computing,Theoretical computer science,Fault tolerance,Evolutionary programming,Symbolic regression,Scalability
Journal
Volume
Issue
ISSN
46
3
0952-8091
Citations 
PageRank 
References 
11
0.62
17
Authors
5
Name
Order
Citations
PageRank
Xin Du112726.78
Youcong Ni2188.10
Zhiqiang Yao320726.95
Ruliang Xiao4288.42
Datong Xie5203.12