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.
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Wang, W., Li, Q., Qi, X. (2008). Gene Expression Programming Neural Network for Regression and Classification. In: Kang, L., Cai, Z., Yan, X., Liu, Y. (eds) Advances in Computation and Intelligence. ISICA 2008. Lecture Notes in Computer Science, vol 5370. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92137-0_24
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DOI: https://doi.org/10.1007/978-3-540-92137-0_24
Publisher Name: Springer, Berlin, Heidelberg
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