TY  - JOUR
T1  - A Hybrid Applied Optimization Algorithm for Training Multi-Layer Neural Networks in Data Classification
AU  - Örkçü, H. Hasan
AU  - Doğan, Mustafa
AU  - Örkçü, Mediha
PY  - 2015
DA  - February
JF  - Gazi University Journal of Science
PB  - Gazi University
WT  - DergiPark
SN  - 2147-1762
SP  - 115
EP  - 132
VL  - 28
IS  - 1
LA  - en
AB  - Backpropagation algorithm is classical technique used in the training of the artificial neural networks. Since this algorithm has many disadvantages, the training of the neural networks has been implemented with various optimization methods. In this paper, a hybrid intelligent model, i.e., hybridGSA, is developed to training artificial neural networks (ANN) and undertaking data classification problems. The hybrid intelligent system aims to exploit the advantages of genetic and simulated annealing algorithms and, at the same time, alleviate their limitations. To evaluate the effectiveness of the hybridGSA method, three benchmark data sets, i.e., Breast Cancer Wisconsin, Pima Indians Diabetes, and Liver Disorders from the UCI Repository of Machine Learning, and a simulation experiment are used for evaluation. A comparative analysis on the real data sets and simulation data shows that the hybridGSA algorithm may offer efficient alternative to traditional training methods for the classification problem.
KW  - Artificial neural networks
KW  - data classification
KW  - training of neural networks
KW  - genetic algorithm
KW  - simulated annealing
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UR  - https://dergipark.org.tr/en/pub/gujs/issue//97891
L1  - https://dergipark.org.tr/en/download/article-file/83711
ER  -