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Predicting high-performance concrete compressive strength using features constructed by Kaizen Programming

The compressive strength of high-performance concrete (HPC) can be
predicted by a nonlinear function of the proportions of its components.
However, HPC is a complex material, and finding that nonlinear function
is not trivial. Many distinct techniques such as traditional statistical
regression methods and machine learning methods have been used to
solve this task, reaching considerably different levels of accuracy.
In this paper, we employ the recently proposed Kaizen Programming
coupled with classical Ordinary Least Squares (OLS) regression to
find high-quality nonlinear combinations of the original features,
resulting in new sets of features. Those new features are then tested
with various regression techniques to perform prediction. Experimental
results show that the features constructed by our technique provide
significantly better results than the original ones. Moreover, when
compared to similar evolutionary approaches, Kaizen Programming builds
only a small fraction of the number of prediction models, but reaches
similar or better results.

 

Kaizen Programming, Prediction, Linear regression, High performance
concrete, Compressive strength