Optimal Determination of K Constant of Ridge Regression Using a Simple Genetic Algorithm

In the present work, the optimal determination of the constant k of ridge regression (RR) model was developed, since this method of regression permit to reduce the multicollinearity problem and it is a one advantage that takes the ridge regression model over ordinary least square (OLS). The optimal constant k was developed by some technique derived from intelligent systems (genetic algorithm) and some statistics techniques. In this paper we present a comparison between these two methodologies for finding the optimal constant k since this constant givesless variance, giving stability to the estimate coefficients and reduce the multicollinearity problem. The results analyzed by the statistical methods, showed that MSR and R2 have very good performance but theVIF's are greater than genetic algorithm (GA) and theGA reduces the VIF's but reduces R2 and increment the MSR.