DocumentCode
1644809
Title
Graph isomorphisms effect on structure optimization of neural networks
Author
Igel, Christian ; Stagge, Peter
Author_Institution
Inst. fur Neuroinformatik, Ruhr-Univ., Bochum, Germany
Volume
1
fYear
2002
fDate
6/24/1905 12:00:00 AM
Firstpage
142
Lastpage
147
Abstract
Concepts from the graph theory and molecular evolution are proposed for analyzing effects of redundancy induced by graph isomorphisms on the structure optimization of neural networks. It is demonstrated that a graph database that considers isomorphisms can drastically reduce the number of evaluations in an evolutionary structure optimization process
Keywords
feedforward neural nets; genetic algorithms; graph theory; multilayer perceptrons; network topology; probability; redundancy; evolutionary algorithms; evolutionary structure optimization; feedforward neural networks; graph isomorphisms; graph theory; molecular evolution; multilayer perceptron; neural networks; probability; topology; Databases; Evolutionary computation; Feedforward neural networks; Graph theory; Labeling; Multilayer perceptrons; Network topology; Neural networks; Neurons; Optimization methods;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location
Honolulu, HI
ISSN
1098-7576
Print_ISBN
0-7803-7278-6
Type
conf
DOI
10.1109/IJCNN.2002.1005459
Filename
1005459
Link To Document