1. A method for optimizing a regression model which predicts a signal as a function of a set of available signals, the method comprising: receiving training data for the set of available signals from a computer system during normal fault-free operation; receiving an objective function which can be used to evaluate how well a regression model predicts the signal; initializing a pool of candidate regression models which includes at least two candidate regression models, wherein each candidate regression model in the pool includes a subset of the set of available signals; and optimizing the regression model by iteratively, selecting two regression models U and V from the pool of candidate regression models, wherein regression models Uand Vbest predict the signal based on the training data and the objective function; using a genetic technique to create an offspring regression model W from U and V by combining parts of the two regression models U and V; and adding W to the pool of candidate regression models.