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Title |
Study of genetic algorithm to fully-automate the design and training of artificial neural network |
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Author |
Osman Ahmed, Mohd Nordin, Suziah Sulaiman, Wan Fatimah |
| Citation |
Vol. 9 No. 1 pp. 217-226 |
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Abstract |
Optimization of artificial neural network (ANN) parameters design for full-automation ability is an extremely important task, therefore it is challenging and daunting task to find out which is effective and accurate method for ANN prediction and optimization. This paper presents different procedures for the optimization of ANN with aim to: solve the time-consuming of learning process, enhancing generalizing ability, achieving robust and accurate model, and to reduce the computational complexity. A Genetic Algorithm (GA) has been used to optimize operational parameters (input variables), and we plan to optimize neural network architecture (i.e. number of hidden layer and neurons per layer), weight, types, training algorithms, activation functions, learning rate, momentum rate, number of iterations, and dataset partitioning ratio. A hybrid neural network and genetic algorithm model for the determination of optimal operational parameter settings based on the proposed approach was developed. The preliminary result of the model has indicated that the new model can optimize operational parameters precisely and quickly, subsequently, satisfactory performance. |
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Keywords |
Artificial Neural network, Genetic algorithm, Optimization |
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