Training feedforward neural networks with Bayesian hyper-heuristics

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dc.contributor.author Schreuder, Arné
dc.contributor.author Bosman, Anna Sergeevna
dc.contributor.author Engelbrecht, Andries P.
dc.contributor.author Cleghorn, Christopher W.
dc.date.accessioned 2024-10-31T12:46:34Z
dc.date.available 2024-10-31T12:46:34Z
dc.date.issued 2025-01
dc.description DATA AVAILABILITY: Data will be made available on request. en_US
dc.description.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. en_US
dc.description.department Computer Science en_US
dc.description.librarian hj2024 en_US
dc.description.sdg SDG-09: Industry, innovation and infrastructure en_US
dc.description.uri http://www.elsevier.com/locate/ins en_US
dc.identifier.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. en_US
dc.identifier.issn 0020-0255 (print)
dc.identifier.issn 1872-6291 (online)
dc.identifier.other 10.1016/j.ins.2024.121363
dc.identifier.uri http://hdl.handle.net/2263/98873
dc.language.iso en en_US
dc.publisher Elsevier en_US
dc.rights © 2024 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). en_US
dc.subject Feedforward neural network (FFNN) en_US
dc.subject Bayesian hyper-heuristic (BHH) en_US
dc.subject Hyper-heuristics en_US
dc.subject Meta-learning en_US
dc.subject Supervised learning en_US
dc.subject Bayesian statistics en_US
dc.subject SDG-09: Industry, innovation and infrastructure en_US
dc.title Training feedforward neural networks with Bayesian hyper-heuristics en_US
dc.type Article en_US


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