RT Journal Article
T1 Random Error Sampling-based Recurrent Neural Network Architecture Optimization.
A1 Camero Unzueta, Andres
A1 Toutouh-el-Alamin, Jamal
A1 Alba-Torres, Enrique
K1 Computación evolutiva
K1 Redes neuronales (Informática)
K1 Programación heurística
AB Recurrent neural networks are good at solving prediction problems. However, finding a network that suits a problem is quite hard because their performance is strongly affected by their architecture configuration. Automatic architecture optimization methods help to find the most suitable design, but they are not extensively adopted because of their high computational cost. In this work, we introduce the Random Error Sampling-based Neuroevolution (RESN), an evolutionary algorithm that uses the mean absolute error random sampling, a training-free approach to predict the expected performance of an artificial neural network, to optimize the architecture of a network. We empirically validate our proposal on four prediction problems, and compare our technique to training-based architecture optimization techniques, neuroevolutionary approaches, and expert designed solutions. Our findings show that we can achieve state-of-the-art error performance and that we reduce by half the time needed to perform the optimization.
PB Elsevier
YR 2020
FD 2020-09-15
LK https://hdl.handle.net/10630/32556
UL https://hdl.handle.net/10630/32556
LA eng
NO Camero, A., Toutouh, J., & Alba, E. (2020). Random error sampling-based recurrent neural network architecture optimization. Engineering Applications of Artificial Intelligence, 96, 103946.
NO Política de acceso abierto tomada de: https://v2.sherpa.ac.uk/id/publication/4626
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 21 ene 2026