Simultaneous Optimization of Weights and Structure of an RBF Neural Network
We propose here a new evolutionary algorithm, the RBF-Gene algorithm, to optimize Radial Basis Function Neural Networks. Unlike other works on this subject, our algorithm can evolve both the structure and the numerical parameters of the network: it is able to evolve the number of neurons and their w...
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Published in | Artificial Evolution pp. 49 - 60 |
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Main Authors | , , , |
Format | Book Chapter |
Language | English |
Published |
Berlin, Heidelberg
Springer Berlin Heidelberg
2006
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Series | Lecture Notes in Computer Science |
Subjects | |
Online Access | Get full text |
ISBN | 3540335897 9783540335894 |
ISSN | 0302-9743 1611-3349 |
DOI | 10.1007/11740698_5 |
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Abstract | We propose here a new evolutionary algorithm, the RBF-Gene algorithm, to optimize Radial Basis Function Neural Networks. Unlike other works on this subject, our algorithm can evolve both the structure and the numerical parameters of the network: it is able to evolve the number of neurons and their weights.
The RBF-Gene algorithm’s behavior is shown on a simple toy problem, the 2D sine wave. Results on a classical benchmark are then presented. They show that our algorithm is able to fit the data very well while keeping the structure simple – the solution can be applied generally. |
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AbstractList | We propose here a new evolutionary algorithm, the RBF-Gene algorithm, to optimize Radial Basis Function Neural Networks. Unlike other works on this subject, our algorithm can evolve both the structure and the numerical parameters of the network: it is able to evolve the number of neurons and their weights.
The RBF-Gene algorithm’s behavior is shown on a simple toy problem, the 2D sine wave. Results on a classical benchmark are then presented. They show that our algorithm is able to fit the data very well while keeping the structure simple – the solution can be applied generally. |
Author | Beslon, Guillaume Favrel, Joël Lefort, Virginie Knibbe, Carole |
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Copyright | Springer-Verlag Berlin Heidelberg 2006 |
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Editor | Collet, Pierre Schoenauer, Marc Liardet, Pierre Lutton, Evelyne Talbi, El-Ghazali |
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Snippet | We propose here a new evolutionary algorithm, the RBF-Gene algorithm, to optimize Radial Basis Function Neural Networks. Unlike other works on this subject,... |
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SubjectTerms | Genetic Algorithm Genetic Code Good Individual Hide Neuron Radial Basis Function Neural Network |
Title | Simultaneous Optimization of Weights and Structure of an RBF Neural Network |
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