System and method for dynamic learning control in genetically en

Data processing: artificial intelligence – Machine learning – Genetic algorithm and genetic programming system

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706 10, 706 15, 706 25, 706 59, G06F 1518, G06E 100, G06E 300

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active

058324666

ABSTRACT:
In the design and implementation of neural networks, training is determined by a series of architectural and parametric decisions. A method is disclosed that, using genetic algorithms, improves the training characteristics of a neural network. The method begins with a population and iteratively modifies one or more parameters in each generation based on the network with the best training response in the previous generation.

REFERENCES:
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patent: 5214746 (1993-05-01), Fogel et al.
patent: 5245696 (1993-09-01), Stork et al.
P. Adamidis and V. Petridis, "Co-operating Populations with Different Evolution Behaviours,"Proceedings of 1996 IEEE Int'l Conference on Evolutionary Computation, pp 188-191, May. 1996.
V. Maniezzo, "Genetic Evolution of the Topology and Weight Distribution of Neural Networks,"IEEE Transactions on Neural Networks, vol. 5, No. 1, pp. 39-53, Jan. 1994.
B. Choi and K. Bluff, "Genetic Optimisation of Control Parameters of a Neural Network, "1995 New Zealand Int'l Two-Stream Conference on Artificial Neural Networks, pp. 174-177, 1995.

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