Discovery of Neural Network Learning Rules Using Genetic Programming
نویسنده
چکیده
Using Genetic Programming Amr Mohamed Radi and Riccardo Poli School of Computer Science The University of Birmingham Birmingham B15 2TT, UK E-mail: fA.M.Radi,[email protected] Technical Report CSRP-97-21 September 3, 1997 Abstract The development of the backpropagation learning rule has been a landmark in neural networks. It provides a computational method for training multilayer networks. Unfortunately, backpropagation su ers from several problems. In this paper, a new technique based upon Genetic Programming (GP) is proposed to overcome some of these problems. We have used GP to discover new supervised learning algorithms. A set of such learning algorithms has been compared with the Standard BackPropagation (SBP) learning algorithm on di erent problems and has been shown to provide better performances. This study indicates that there exist many supervised learning algorithms better than, but similar to, SBP and that GP can be used to discover them.
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