A Self-Adaptive Hybrid Bat Algorithm for Training Feedforward Neural Networks

Training feedforward neural network (FFNN) is a complex task in the supervised learning field. An FFNN trainer aims to find the best set of weights that minimizes classification error. This paper presents a new training method based on hybrid bat optimization with self-adaptive differential evolution to train the feedforward neural networks. The hybrid training algorithm combines bat and the self-adaptive differential evolution algorithm called BAT-SDE. BAT-SDE is used to better search in the solution space, which proves its effectiveness in large space solutions. The performance of the proposed approach was compared with eight evolutionary techniques and the standard momentum backpropagation and adaptive learning rate. The comparison was benchmarked and evaluated using seven bio-medical datasets and one large credit card fraud detection dataset. The results of the comparative study show that BAT-SDE outperformed other training methods in most datasets and can be an alternative to other training methods.