Evolutionary polynomial regression algorithm combined with robust bayesian regression

Recent developments in the fields of scientific computation and Machine Learning (ML) techniques have led to a proliferation of algorithms capable of interpreting data and predict results. Among the others, the Evolutionary Polynomial Regression (EPR) has gained particular attention for many engineering applications. This paper presents a novel robust Evolutionary Polynomial Bayesian Regression (EPBR) algorithm. The optimal polynomial structure is selected using GAs. The model parameters are assumed as random variables whose posterior distributions are assessed by a robust Bayesian regression algorithm. To reduce the effects of the outliers, the model disturbance is described by a Student -t distribution whose degrees of freedoms are sampled from an objective prior probability density function. The proposed technique is applied to a dataset consisting of experimental shear strength values related to Reinforced Concrete (RC) beams without stirrups. The optimal EPBR model is compared with different experimental and design formulations to emphasize its accuracy and consistency.

Automated summary

Recent developments in scientific computation and Machine Learning have led to the emergence of algorithms capable of interpreting data and predicting results. This paper introduces a robust Evolutionary Polynomial Bayesian Regression (EPBR) algorithm and applies it to experimental shear strength data of Reinforced Concrete (RC) beams.