Parameter genetic learning of perceptron networks

Authors:
Roman Neruda;Stanislav Slušný
Affiliations:
Institute of Computer Science, Academy of Sciences of the Czech Republic, Prague 8, Czech Republic;Institute of Computer Science, Academy of Sciences of the Czech Republic, Prague 8, Czech Republic
Venue:
ICS'06 Proceedings of the 10th WSEAS international conference on Systems
Year:
2006

Citing 5
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Abstract

This paper reviews different combinations between the most widely used type of neural networks -- a multi-layer perceptron -- and evolutionary algorithms. Several methods to train the weights of the network are tested using a real-world classification problems from Proben1 benchmark suite. It is shown, that combining evolutionary algorithms with neural networks can lead to better results than relying on neural networks alone. Comparison to gradient algorithms is discussed.