The role of weight domain in evolutionary design of multilayer perceptrons

Maciej Grzenda , B Macukow


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 at 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 / DACSCM)
Maciej Grzenda,,
- Department of Applied Computer Science and Computation Methods
, B Macukow
B Macukow,,
Pages596-599 vol.6
Book IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, 2000, vol. 6, 2000
Keywords in Englishcomputational complexity, connection weights, evolutionary computation, evolutionary design, evolutionary programming, genotype precision, iterative changes, iterative methods, learning (artificial intelligence), multilayer perceptrons, network architecture, search efficiency, search problems, weight adjustment
Score (nominal)3
Publication indicators WoS Citations = 0
Citation count*2 (2016-03-04)
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