Training feedforward neural networks with Bayesian hyper-heuristics
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Date
Authors
Schreuder, Arné
Bosman, Anna Sergeevna
Engelbrecht, Andries P.
Cleghorn, Christopher W.
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier
Abstract
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.
Description
DATA AVAILABILITY:
Data will be made available on request.
Keywords
Feedforward neural network (FFNN), Bayesian hyper-heuristic (BHH), Hyper-heuristics, Meta-learning, Supervised learning, Bayesian statistics, SDG-09: Industry, innovation and infrastructure
Sustainable Development Goals
SDG-09: Industry, innovation and infrastructure
Citation
Schreuder, A.N., Bosman, A.S., Engelbrecht, A.P. et al. 2025, 'Training feedforward neural networks with Bayesian hyper-heuristics', Information Sciences, vol. 686, art. 121363, pp. 1-16, doi : 10.1016/j.ins.2024.121363.