Selection of multiple regularization parameters in local ridge regression using evolutionary algorithms and prediction risk optimization

This article presents a new methodology for regularizing data-based predictive models. Traditional modeling using regression can produce unrepeatable, unstable, or noisy predictions when the inputs are highly correlated. Ridge regression is a regularization technique commonly used to deal with those problems. In some practical situations, regularization methods using multiple parameters have advantages. The methodology proposed in this article optimizes several local regularization parameters that operate independently on each component. This method allows components with significant predictive power to be passed while components with low predictive power are damped. The optimal combination of regularization parameters are computed using an Evolutionary Strategy search technique with the objective function being a predictive error estimate. Two examples demonstrate this technique's advantages.