Performance comparison of gradient descent and Genetic Algorithm based Artificial Neural Networks training
Fadzil Ahmad, Nor Ashidi Mat Isa, Muhammad Khusairi Osman, Zakaria Hussain · 2010
One of the major issues concerning the Artificial Neural Networks (ANNs) design is a proper adjustment of the weights of the network. There have been a number of studies comparing the performance of evolutionary and gradient based ANNs learning. But the results of the studies, sometime conflicting to each other although the same and standard dataset development had been used. Motivated by this finding, the main objective of this paper is to make another comparison between the variations of gradient descent and Genetic Algorithm (GA) based ANNs training with special emphasize given on the developed algorithm and comparison methodology. Besides, the effect of the crossover operation on GA training is also being investigated. The comparison is done using cancer and diabetes benchmark dataset. The result shows that the overall classification error percentage of the family of GA is slightly better than those of gradient descent on cancer dataset. On the other hand, gradient descent is much better than GA on diabetes.