Evolutionary polynomial regression improved by regularization methods

Yao Li, Mo Li and Lei Zhang

PLOS ONE, 2023, vol. 18, issue 2, 1-21

Abstract: Evolutionary polynomial regression (EPR) is a data mining tool that has been widely used in solving various geotechnical engineering problems. The fitness function is the core of EPR. However, overfitting may still occur in EPR, and this issue may cause the testing dataset not to perform effectively. Improvement of the EPR fitness function through L1 and L2 regularization methods is critical to avoid overfitting and enhance good generalization. First, the appropriate values of the regularization parameter λ of the L1 regularization method (L1RM) and L2 regularization method (L2RM) are determined by comparing the test data sets. Then, the EPR with a classical fitness function is compared with that of L1 or L2 regularization methods to evaluate their abilities in developing regression and producing accurate predictions. The results show that the fitness function combined with the regularization method could improve the EPR. However, L1RM performs better in prediction than L2RM. Improvement of EPR using L1RM could solve problems associated with construction constitutive models or could be used for prediction in geotechnical engineering.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0282029

DOI: 10.1371/journal.pone.0282029

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