TY - GEN
T1 - Single-step-ahead and multi-step-ahead prediction with evolutionary artificial neural networks
AU - Landassuri-Moreno, Víctor Manuel
AU - Bustillo-Hernández, Carmen L.
AU - Carbajal-Hernández, José Juan
AU - Sańchez Fernández, Luis P.
PY - 2013
Y1 - 2013
N2 - In recent years, Evolutionary Algorithms (EAs) have been remarkably useful to improve the robustness of Artificial Neural Networks (ANNs). This study introduces an experimental analysis using an EAs aimed to evolve ANNs architectures (the FS-EPNet algorithm) to understand how neural networks are evolved with a steady-state algorithm and compare the Single-step-ahead (SSP) and Multiple-step-ahead (MSP) methods for prediction tasks over two test sets. It was decided to test an inside-set during evolution and an outside-set after the whole evolutionary process has been completed to validate the generalization performance with the same method (SSP or MSP). Thus, the networks may not be correctly evaluated (misleading fitness) if the single SSP is used during evolution (inside-set) and then the MSP at the end of it (outside-set). The results show that the same prediction method should be used in both evaluation sets providing smaller errors on average.
AB - In recent years, Evolutionary Algorithms (EAs) have been remarkably useful to improve the robustness of Artificial Neural Networks (ANNs). This study introduces an experimental analysis using an EAs aimed to evolve ANNs architectures (the FS-EPNet algorithm) to understand how neural networks are evolved with a steady-state algorithm and compare the Single-step-ahead (SSP) and Multiple-step-ahead (MSP) methods for prediction tasks over two test sets. It was decided to test an inside-set during evolution and an outside-set after the whole evolutionary process has been completed to validate the generalization performance with the same method (SSP or MSP). Thus, the networks may not be correctly evaluated (misleading fitness) if the single SSP is used during evolution (inside-set) and then the MSP at the end of it (outside-set). The results show that the same prediction method should be used in both evaluation sets providing smaller errors on average.
KW - Artificial neural networks
KW - EANNs
KW - Evolutionary algorithms
KW - Multi-step-ahead prediction
KW - Single-step-ahead prediction
UR - http://www.scopus.com/inward/record.url?scp=84893190926&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-41822-8_9
DO - 10.1007/978-3-642-41822-8_9
M3 - Contribución a la conferencia
SN - 9783642418211
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 65
EP - 72
BT - Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 18th Iberoamerican Congress, CIARP 2013, Proceedings
T2 - 18th Iberoamerican Congress on Pattern Recognition, CIARP 2013
Y2 - 20 November 2013 through 23 November 2013
ER -