A new hybrid neural-genetic methodology for improving learning

Authors:
A. Likartsis
Affiliations:
-
Venue:
ICTAI '97 Proceedings of the 9th International Conference on Tools with Artificial Intelligence
Year:
1997

Citing 0
Cited 1

Conquering the Needle-in-a-Haystack: How Correlated Input Variables Beneficially Alter the Fitness Landscape for Neural Networks

EvoBIO '09 Proceedings of the 7th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics

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Abstract

Abstract: A new hybrid neural-generic methodology is presented that exploits the optimization advantages of genetic algorithms for the purpose of accelerating neural network training. The choice of fitness function is addressed and experimental findings are shown where neural network training is improved through the proposed approach. The results suggest that genetic algorithms can be a powerful tool for improving learning in neural networks.