1. A computer program product, the computer program product being tangibly embodied on a non-transitory computer-readable storage medium and comprising instructions that, when executed, are configured to cause at least one processor to: determine a model vector <d , w , δ h >, in which d h represents a feature vector including t feature subsets of a feature set, w h represents a weighted model vector including t weighted automated learning models, and δ h represents t parameter sets parameterizing the w is weighted automated learning models; adjust weights of w to obtain an updated w , w h+1 , based on performance evaluations of the t weighted automated learning models, and based on w ; search a feature solution space to obtain t updated feature subsets of the feature set, to thereby obtain an updated d h , d h+1 ; search a parameter solution space to obtain t updated parameter sets, to thereby obtain an updated δ h , δ h+1 ; and determine an optimized model vector (d h+1 , w h+1 , δ h+1 ).