A Novel Two-Level Clustering-Based Differential Evolution Algorithm for Training Neural Networks

Determining appropriate weights and biases for feed-forward neural networks is a critical task. Despite the prevalence of gradient-based methods for training, these approaches suffer from sensitivity to initial values and susceptibility to local optima. To address these challenges, we introduce a no...

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Hauptverfasser: Mousavirad, Seyed Jalaleddin, Oliva, Diego, Schaefer, Gerald, Moghadam, Mahshid Helali, El-Abd, Mohammed
Format: Buchkapitel
Sprache:eng
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Zusammenfassung:Determining appropriate weights and biases for feed-forward neural networks is a critical task. Despite the prevalence of gradient-based methods for training, these approaches suffer from sensitivity to initial values and susceptibility to local optima. To address these challenges, we introduce a novel two-level clustering-based differential evolution approach, C2L-DE, to identify the initial seed for a gradient-based algorithm. In the initial phase, clustering is employed to detect some regions in the search space. Population updates are then executed based on the information available within each region. A new central point is proposed in the subsequent phase, leveraging cluster centres for incorporation into the population. Our C2L-DE algorithm is compared against several recent DE-based neural network training algorithms, and is shown to yield favourable performance.
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-031-56852-7_17