Genetic programming discovers efficient learning rules for the hidden and output layers of feedforward neural networks

Amr Radi, Riccardo Poli

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (SciVal)


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 algorithms 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 performances. This study indicates that there exist many supervised learning algorithms better than SBP and that GP can be used to discover them.

Original languageEnglish
Title of host publicationGenetic Programming - 2nd European Workshop, EuroGP 1999, Proceedings
EditorsRiccardo Poli, Peter Nordin, William B. Langdon, Terence C. Fogarty
PublisherSpringer Verlag
Number of pages15
ISBN (Print)3540658998, 9783540658993
Publication statusPublished - 1999
Externally publishedYes
Event2nd European Workshop on Genetic Programming, EuroGP 1999 - Goteborg, Sweden
Duration: May 26 1999May 27 1999

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference2nd European Workshop on Genetic Programming, EuroGP 1999

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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