By Topic

Function Finding Using Gene Expression Programming Based Neural Network

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Qu Li ; Software Coll., Zhejiang Univ. of Technol., Hangzhou ; Weihong Wang ; Xing Qi ; Bo Chen
more authors

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 other data mining as well as machine learning tasks. However, GEP's potential for neural network learning has not been well studied. In this paper, we prove that GEP neural network (GEPNN) is not able to solve high order regression problems. Based on our proof, we propose an extended method for evolving neural network with GEP. The extended GEPNN is used in various kinds of function finding problems. Results on multiple leaning methods show the effectiveness of our method.

Published in:

Natural Computation, 2008. ICNC '08. Fourth International Conference on  (Volume:3 )

Date of Conference:

18-20 Oct. 2008