Single-step-ahead and multi-step-ahead prediction with evolutionary artificial neural networks

Víctor Manuel Landassuri-Moreno, Carmen L. Bustillo-Hernández, José Juan Carbajal-Hernández, Luis P. Sańchez Fernández

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationProgress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 18th Iberoamerican Congress, CIARP 2013, Proceedings
Pages65-72
Number of pages8
EditionPART 1
DOIs
StatePublished - 2013
Event18th Iberoamerican Congress on Pattern Recognition, CIARP 2013 - Havana, Cuba
Duration: 20 Nov 201323 Nov 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume8258 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference18th Iberoamerican Congress on Pattern Recognition, CIARP 2013
Country/TerritoryCuba
CityHavana
Period20/11/1323/11/13

Keywords

  • Artificial neural networks
  • EANNs
  • Evolutionary algorithms
  • Multi-step-ahead prediction
  • Single-step-ahead prediction

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