Patent ID: 7349823

Claim:
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 some or all of the set of n available signals; optimizing the regression model by iteratively selecting two regression models U and V from the pool of candidate regression models, wherein regression models U and V best 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 some of the signals included in the two regression models U and V by selecting a crossover point k, wherein k is an integer between 1 and n; creating W by combining the signals included in U and V such that the first k signals of the n available signals included in W match the first k signals included in U, and the last (n-k) signals included in W match the last (n-k) signals included in V; reversing the inclusion of one signal in W with respect to the inclusion of the signal in U or V with a predetermined probability p; and adding W to the pool of candidate regression models; upon determining that a stopping criteria has been satisfied by the two regression models U and V, using the objective function to select one of the regression models U and V that best predicts the signal; and using the selected regression model to predict a value of the signal during operation of the computer system.