Forecasting Livestock Activity through Interpretable Neuroevolutionary Transfer Learning

In this paper, we describe a neuroevolutionary approach to livestock activity forecasting, specifically targeting the prediction of Iberian pigs movements. We successfully integrated Transfer Learning to save computational time and used an Explainable Artificial Intelligence technique to provide val...

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Detalles Bibliográficos
Autores: Vellinger, Aymeric, Rodrı́guez Dı́az, Francesc, Divina, Federico, Torres Maldonado, José Francisco
Tipo de recurso: artículo
Fecha de publicación:2026
País:España
Institución:Universidad Pablo de Olavide (UPO)
Repositorio:RIO. Repositorio Institucional Olavide
Idioma:inglés
OAI Identifier:oai:rio.upo.es:10433/26300
Acceso en línea:https://hdl.handle.net/10433/26300
Access Level:acceso embargado
Palabra clave:Time series forecasting
Neuroevolution
Deep Learning
Explainable Artificial Intelligence
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spelling Forecasting Livestock Activity through Interpretable Neuroevolutionary Transfer LearningVellinger, AymericRodrı́guez Dı́az, FrancescDivina, FedericoTorres Maldonado, José FranciscoTime series forecastingNeuroevolutionDeep LearningExplainable Artificial IntelligenceIn this paper, we describe a neuroevolutionary approach to livestock activity forecasting, specifically targeting the prediction of Iberian pigs movements. We successfully integrated Transfer Learning to save computational time and used an Explainable Artificial Intelligence technique to provide valuable insights from the model predictions. Inspired by previous work, we employ Deep Evolutionary Network Structured Representation to optimize both Long Short-Term Memory networks and Convolutional Neural Networks using genetic algorithms and dynamic structured grammatical evolution, and we compare the results with other commonly used approaches for time series forecasting. Experimental results demonstrate the superior performance of the proposed Long Short-Term Memory models over more traditional methods, highlighting their precision and consistency in predicting livestock activities. Furthermore, the application of Explainable Artificial Intelligence techniques enable to gain a deeper understanding and trust in AI-driven decisions within precision livestock farming.Oxford University Press20262026-02-2720262026-02-1620262026-02-16journal articlehttp://purl.org/coar/resource_type/c_6501AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/10433/26300reponame:RIO. Repositorio Institucional Olavideinstname:Universidad Pablo de Olavide (UPO)InglésengAgencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023 PID2023-146037OB-C22 APRENDIZAJE AUTOMATICO SOSTENIBLE PARA AGUA Y CAMBIO CLIMATICOembargoed accesshttp://purl.org/coar/access_right/c_f1cfAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/embargoedAccessoai:rio.upo.es:10433/263002026-06-13T12:46:27Z
dc.title.none.fl_str_mv Forecasting Livestock Activity through Interpretable Neuroevolutionary Transfer Learning
title Forecasting Livestock Activity through Interpretable Neuroevolutionary Transfer Learning
spellingShingle Forecasting Livestock Activity through Interpretable Neuroevolutionary Transfer Learning
Vellinger, Aymeric
Time series forecasting
Neuroevolution
Deep Learning
Explainable Artificial Intelligence
title_short Forecasting Livestock Activity through Interpretable Neuroevolutionary Transfer Learning
title_full Forecasting Livestock Activity through Interpretable Neuroevolutionary Transfer Learning
title_fullStr Forecasting Livestock Activity through Interpretable Neuroevolutionary Transfer Learning
title_full_unstemmed Forecasting Livestock Activity through Interpretable Neuroevolutionary Transfer Learning
title_sort Forecasting Livestock Activity through Interpretable Neuroevolutionary Transfer Learning
dc.creator.none.fl_str_mv Vellinger, Aymeric
Rodrı́guez Dı́az, Francesc
Divina, Federico
Torres Maldonado, José Francisco
author Vellinger, Aymeric
author_facet Vellinger, Aymeric
Rodrı́guez Dı́az, Francesc
Divina, Federico
Torres Maldonado, José Francisco
author_role author
author2 Rodrı́guez Dı́az, Francesc
Divina, Federico
Torres Maldonado, José Francisco
author2_role author
author
author
dc.contributor.none.fl_str_mv
dc.subject.none.fl_str_mv Time series forecasting
Neuroevolution
Deep Learning
Explainable Artificial Intelligence
topic Time series forecasting
Neuroevolution
Deep Learning
Explainable Artificial Intelligence
description In this paper, we describe a neuroevolutionary approach to livestock activity forecasting, specifically targeting the prediction of Iberian pigs movements. We successfully integrated Transfer Learning to save computational time and used an Explainable Artificial Intelligence technique to provide valuable insights from the model predictions. Inspired by previous work, we employ Deep Evolutionary Network Structured Representation to optimize both Long Short-Term Memory networks and Convolutional Neural Networks using genetic algorithms and dynamic structured grammatical evolution, and we compare the results with other commonly used approaches for time series forecasting. Experimental results demonstrate the superior performance of the proposed Long Short-Term Memory models over more traditional methods, highlighting their precision and consistency in predicting livestock activities. Furthermore, the application of Explainable Artificial Intelligence techniques enable to gain a deeper understanding and trust in AI-driven decisions within precision livestock farming.
publishDate 2026
dc.date.none.fl_str_mv 2026
2026-02-27
2026
2026-02-16
2026
2026-02-16
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
AM
http://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/10433/26300
url https://hdl.handle.net/10433/26300
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Agencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023 PID2023-146037OB-C22 APRENDIZAJE AUTOMATICO SOSTENIBLE PARA AGUA Y CAMBIO CLIMATICO
dc.rights.none.fl_str_mv embargoed access
http://purl.org/coar/access_right/c_f1cf
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/embargoedAccess
rights_invalid_str_mv embargoed access
http://purl.org/coar/access_right/c_f1cf
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv embargoedAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Oxford University Press
publisher.none.fl_str_mv Oxford University Press
dc.source.none.fl_str_mv reponame:RIO. Repositorio Institucional Olavide
instname:Universidad Pablo de Olavide (UPO)
instname_str Universidad Pablo de Olavide (UPO)
reponame_str RIO. Repositorio Institucional Olavide
collection RIO. Repositorio Institucional Olavide
repository.name.fl_str_mv
repository.mail.fl_str_mv
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