Role of weight domain in evolutionary design of multilayer perceptrons

Maciej Grzenda , Bohdan Macukow

Abstract

Among different models of neural networks multilayer perceptrons play an important role. Most training methods, including back-propagation concentrate on weight adjustment only. Still the performance of the network strongly depends on its architecture. In our paper the algorithm based on evolutionary programming is proposed. Unlike most other methods of this type, the genotype precision is being evolved together with the architecture and connection weights of the network. Iterative changes in the weight domain make the network structure rough only first so as to tune it later. Not only does it help to avoid inadequate weight precision, but also the search efficiency is increased. Different aspects of the weight set selection are investigated and discussed.

Author Maciej Grzenda (FMIS / DIPS)
Maciej Grzenda,,
- Department of Information Processing Systems
, Bohdan Macukow (FMIS / DIPS)
Bohdan Macukow,,
- Department of Information Processing Systems
Pages596-599
Book Amari Shun-Ichi (eds.): Neural computing: new challenges and perspectives for the new millennium : proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, IJCNN 2000, Como, Italy, 24 - 27 July 2000 , vol. 6, 2000, IEEE Computer Society , ISBN 0-7695-0619-4,
Languageen angielski
Score (nominal)0
Publication indicators Scopus Citations = 3; WoS Citations = 0
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