Schema theory based data engineering in gene expression programming for big data analytics

Huang, Zhengwen, Li, Maozhen, Chousidis, Christos ORCID:, Mousavi, Ali and Jiang, Changjun (2017) Schema theory based data engineering in gene expression programming for big data analytics. IEEE Transactions on Evolutionary Computation, 22 (5). pp. 792-804. ISSN 1089-778X

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Gene expression programming (GEP) is a data driven evolutionary technique that well suits for correlation mining. Parallel GEPs are proposed to speed up the evolution process using a cluster of computers or a computer with multiple CPU cores. However, the generation structure of chromosomes and the size of input data are two issues that tend to be neglected when speeding up GEP in evolution. To fill the research gap, this paper proposes three guiding principles to elaborate the computation nature of GEP in evolution based on an analysis of GEP schema theory. As a result, a novel data engineered GEP is developed which follows closely the generation structure of chromosomes in parallelization and considers the input data size in segmentation. Experimental results on two data sets with complementary features show that the data engineered GEP speeds up the evolution process significantly without loss of accuracy in data correlation mining. Based on the experimental tests, a computation model of the data engineered GEP is further developed to demonstrate its high scalability in dealing with potential big data using a large number of CPU cores.

Item Type: Article
Identifier: 10.1109/TEVC.2017.2771445
Subjects: Computing > Systems > Distributed computing
Computing > Software engineering
Depositing User: Christos Chousidis
Date Deposited: 13 Feb 2018 15:49
Last Modified: 06 Feb 2024 15:56


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