Improving kernel semi-parametric regression model based on a bat optimization algorithm.

The accuracy of the work done by several semi-parametric regression models, which are used to address issues in various fields such as economics, medicine, etc., is determined by a set of criteria and conditions such as the speed of access to results and their accuracy. Several evolutionary algorithms have been used which have good results, in this manuscript, the bat algorithm (BA) is used in a separate space to determine the smoothing coefficient used in these models. Where the approach finds the optimal values of the smoothing coefficient by first filling the initial population with random values and then using the kernel function to analyze the results of this approach, which showed more accurate results and took less time than the rest of the compared methods such as CV and GCV. [ABSTRACT FROM AUTHOR]