DENSER: deep evolutionary network structured representation. Deep Evolutionary Network Structured Representation (DENSER) is a novel approach to automatically design Artificial Neural Networks (ANNs) using Evolutionary Computation. The algorithm not only searches for the best network topology, but also tunes hyper-parameters (e.g., learning or data augmentation parameters). The automatic design is achieved using a representation with two distinct levels, where the outer level encodes the general structure of the network, and the inner level encodes the parameters associated with each layer. The allowed layers and hyper-parameter value ranges are defined by means of a human-readable Context-Free Grammar.
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References in zbMATH (referenced in 2 articles )
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- Gu, Xue; Meng, Ziyao; Liang, Yanchun; Xu, Dong; Huang, Han; Han, Xiaosong; et al.: ESAE: evolutionary strategy-based architecture evolution (2020)
- Mouratidis, Despoina; Kermanidis, Katia Lida: Ensemble and deep learning for language-independent automatic selection of parallel data (2019)