Robust regression and outlier detection for non-linear models using genetic algorithms

Experimental data such as calibration and pharmacokinetic data can be contaminated with outliers. Robust regression based on the calculation of the least median of squared residuals (LMS) is robust to the presence of outliers and is used for outlier detection. The original LMS program only handles models which are linear in the parameters. A genetic algorithm can be used to obtain the LMS parameters for models that are non-linear in the parameters. In this work the feasibility of using genetic algorithms for LMS is demonstrated by means of curved analytical calibration and pharmacokinetic data contaminated with outliers.