Gene Expression Programming Neural Network for Regression and Classification

Authors:
Weihong Wang;Qu Li;Xing Qi
Affiliations:
Software College, Zhejiang University of Technology, Hangzhou, China 310032;Software College, Zhejiang University of Technology, Hangzhou, China 310032;Software College, Zhejiang University of Technology, Hangzhou, China 310032
Venue:
ISICA '08 Proceedings of the 3rd International Symposium on Advances in Computation and Intelligence
Year:
2008

Citing 3
Cited 0

Data mining: practical machine learning tools and techniques with Java implementations

Data mining: practical machine learning tools and techniques with Java implementations
Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence (Studies in Computational Intelligence)

Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence (Studies in Computational Intelligence)
Evolving accurate and compact classification rules with gene expression programming

IEEE Transactions on Evolutionary Computation

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Abstract

Gene Expression Programming(GEP) is a kind of heuristic method based on evolutionary computation theory. Basic GEP method has been proved to be powerful in symbolic regression and data mining tasks. However, GEP's potential for neural network learning has not been well studied. In this paper, we prove that basic GEP neural network(GEPNN) is unable to solve difficult regression and classification problems. Based on our proof, we propose an extended method for evolving neural network with GEP. The extended GEPNN is used in function finding and classification problems. Results on multiple learning methods show the effectiveness of our method.