Journal
NEUROCOMPUTING
Volume 360, Issue -, Pages 25-36Publisher
ELSEVIER
DOI: 10.1016/j.neucom.2019.03.097
Keywords
Artificial neural network; Evolutionary neural network; Differential evolution; Levenberg-Marquardt method; Generalization ability
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Funding
- Natural Science Basic Research Plan in Shaanxi Province of China [2019JM-503]
- National Natural Science Foundation of China [51875432]
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This paper presents a two-stage approach, denoted as CBRDE-LM, to evolve the architecture and weights of feedforward artificial neural networks. In the first stage, a collaborative binary-real differential evolution (CBRDE) is used to optimize simultaneously network architecture and connection weights of an ANN by a specific individual representation and evolutionary scheme, in which the structure is indirectly represented as binary coding and connection weights are directly encoded by real-valued coding. In the second stage, based on the resulting architecture and weights of an ANN, Levenberg-Marquardt (LM) backpropagation algorithm is adopted for fine-tuning ANN weights. The performance of the two-stage approach has been evaluated on several benchmarks. The results demonstrate that the two-stage approach can fast produce compact ANNs with good generalization ability at low computational cost. (C) 2019 The Authors. Published by Elsevier B.V.
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