A Hybrid Applied Optimization Algorithm for Training Multi-Layer Neural Networks in Data Classification

Backpropagation algorithm is a 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 (hybrid Genetic Algorithm and Simulated Annealing), is developed for 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 show that the hybridGSA algorithm may offer efficient alternative to traditional training methods for the classification problem.