%0 Conference Paper %1 radi:1999:GPdelrholffNN %A Radi, Amr %A Poli, Riccardo %B Genetic Programming, Proceedings of EuroGP'99 %C Goteborg, Sweden %D 1999 %E Poli, Riccardo %E Nordin, Peter %E Langdon, William B. %E Fogarty, Terence C. %I Springer-Verlag %K algorithms, genetic programming %P 120--134 %T Genetic Programming Discovers Efficient Learning Rules for the Hidden and Output Layers of Feedforward Neural Networks %U http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1598&spage=120 %V 1598 %X The learning method is critical for obtaining good generalisation in neural networks with limited training data. The Standard BackPropagation (SBP ) training algorithm suffers from several problems such as sensitivity to the initial conditions and very slow convergence. The aim of this work is to use Genetic Programming (GP) to discover new supervised learning algorithms which can overcome some of these problems. In previous research a new learning algorithm for the output layer has been discovered using GP. By comparing this with SBP on different problems better performance was demonstrated. This paper shows that GP can also discover better learning algorithms for the hidden layers to be used in conjunction with the algorithm previously discovered. Comparing these with SBP on different problems we show they provide better performance. This study indicates that there exist many supervised learning algorithms better than SBP and that GP can be used to discover them. %@ 3-540-65899-8

@inproceedings{radi:1999:GPdelrholffNN, abstract = {The learning method is critical for obtaining good generalisation in neural networks with limited training data. The Standard BackPropagation (SBP ) training algorithm suffers from several problems such as sensitivity to the initial conditions and very slow convergence. The aim of this work is to use Genetic Programming (GP) to discover new supervised learning algorithms which can overcome some of these problems. In previous research a new learning algorithm for the output layer has been discovered using GP. By comparing this with SBP on different problems better performance was demonstrated. This paper shows that GP can also discover better learning algorithms for the hidden layers to be used in conjunction with the algorithm previously discovered. Comparing these with SBP on different problems we show they provide better performance. This study indicates that there exist many supervised learning algorithms better than SBP and that GP can be used to discover them.}, added-at = {2008-06-19T17:46:40.000+0200}, address = {Goteborg, Sweden}, author = {Radi, Amr and Poli, Riccardo}, biburl = {https://www.bibsonomy.org/bibtex/2146a27b67e2ac9091b6ccdbf034d1bcc/brazovayeye}, booktitle = {Genetic Programming, Proceedings of EuroGP'99}, editor = {Poli, Riccardo and Nordin, Peter and Langdon, William B. and Fogarty, Terence C.}, interhash = {f9b4134c9d9607bdda5ad5957d4e88e9}, intrahash = {146a27b67e2ac9091b6ccdbf034d1bcc}, isbn = {3-540-65899-8}, keywords = {algorithms, genetic programming}, month = {26-27 May}, notes = {EuroGP'99, part of \cite{poli:1999:GP}}, organisation = {EvoNet}, pages = {120--134}, publisher = {Springer-Verlag}, publisher_address = {Berlin}, series = {LNCS}, timestamp = {2008-06-19T17:50:01.000+0200}, title = {Genetic Programming Discovers Efficient Learning Rules for the Hidden and Output Layers of Feedforward Neural Networks}, url = {http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1598&spage=120}, volume = 1598, year = 1999 }