A dual search‐based EPR with self‐adaptive ofspring creation and compromise programming model selection.

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dc.contributor.authorGomes, Guilherme José Cunha
dc.contributor.authorGomes, Ruan Gonçalves de Souza
dc.contributor.authorVargas Júnior, Eurípedes do Amaral
dc.date.accessioned2022-09-16T17:11:20Z
dc.date.available2022-09-16T17:11:20Z
dc.date.issued2021pt_BR
dc.description.abstractEvolutionary polynomial regression (EPR) is extensively used in engineering for soil properties modeling. This grey-box technique uses evolutionary computing to produce simple, transparent and well-structured models in the form of polynomial equations that best explain the observed data. A key task is then to determine mathematical structures for modeling physical phenomena and to select the optimal EPR model. This requires an algorithm to search through the model structure space and successfully produce feasible solutions that honor a set of statistical metrics. The complexity of EPR models increases greatly, however, with the number of polynomial terms used to tune these models. In this paper, we propose an alternative EPR for modeling complex soil properties. We implement a dual search-based EPR with self-adaptive ofspring creation as model structure search strategy and couple a compromise programming tool to select a model that is preferred statistically relative to models with diferent polynomial terms. We illustrate our method using real-world data to improve predictions of optimal moisture content and creep index for soils. Our results demonstrate that the models derived using the proposed methodology can predict soil properties with adequate accuracy, physical meaning and lower number of parameters and input variables.pt_BR
dc.identifier.citationGOMES, G. J. C.; GOMES, R. G. de S.; VARGAS JÚNIOR, E. do A. A dual search‐based EPR with self‐adaptive ofspring creation and compromise programming model selection. Engineering with Computers, v. 1, mar. 2021. Disponível em: <https://link.springer.com/article/10.1007/s00366-021-01313-x>. Acesso em: 29 abr. 2022.pt_BR
dc.identifier.doihttps://doi.org/10.1007/s00366-021-01313-xpt_BR
dc.identifier.issn1435-5663
dc.identifier.urihttp://www.repositorio.ufop.br/jspui/handle/123456789/15323
dc.identifier.uri2https://link.springer.com/article/10.1007/s00366-021-01313-xpt_BR
dc.language.isoen_USpt_BR
dc.rightsrestritopt_BR
dc.subjectEvolutionary polynomial regressionpt_BR
dc.subjectDiferential evolutionpt_BR
dc.subjectGenetic algorithmspt_BR
dc.subjectSoil propertiespt_BR
dc.titleA dual search‐based EPR with self‐adaptive ofspring creation and compromise programming model selection.pt_BR
dc.typeArtigo publicado em periodicopt_BR
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