1. An automated method for identifying quality-predictive features of a repeatable process, the method comprising: measuring raw time series data during the repeatable process using a set of sensors, wherein the raw time series data describes multiple parameters of the repeatable process; receiving, via a first controller, the raw time series data from the set of sensors; stochastically generating candidate features from the raw time series data using the first controller, wherein the candidate features are predictive of a quality of a work piece manufactured via the repeatable process; determining, via a genetic or evolutionary programming module employing mathematical tools with a symbolic manipulator for stochastic optimization, a predictive features set of the generated candidate features that is more predictive of the quality of the work piece than a predictive candidate features set of the generated candidate features; and executing a control action with respect to the repeatable process via a second controller using only the predictive features set, the control action including determining the quality of the work piece by applying a rule to the predictive features set, and modifying at least one of the work piece and a parameter of the repeatable process based on the determined quality.