Training Feedforward Neural Networks with Bayesian Hyper-Heuristics
This provides an incremental improvement for researchers and practitioners in machine learning by automating heuristic selection in neural network training.
The paper tackles the problem of automating heuristic selection for training feedforward neural networks by introducing a Bayesian hyper-heuristic, which outperforms ten existing heuristics on fourteen classification and regression datasets.
The process of training feedforward neural networks (FFNNs) can benefit from an automated process where the best heuristic to train the network is sought out automatically by means of a high-level probabilistic-based heuristic. This research introduces a novel population-based Bayesian hyper-heuristic (BHH) that is used to train feedforward neural networks (FFNNs). The performance of the BHH is compared to that of ten popular low-level heuristics, each with different search behaviours. The chosen heuristic pool consists of classic gradient-based heuristics as well as meta-heuristics (MHs). The empirical process is executed on fourteen datasets consisting of classification and regression problems with varying characteristics. The BHH is shown to be able to train FFNNs well and provide an automated method for finding the best heuristic to train the FFNNs at various stages of the training process.