A dual search‐based EPR with self‐adaptive ofspring creation and compromise programming model selection.
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2021
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Resumo
Evolutionary 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.
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Evolutionary polynomial regression, Diferential evolution, Genetic algorithms, Soil properties
Citação
GOMES, 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.