1. A modeling system that comprises: a memory; and a processor coupled to the memory and configured to execute software stored in said memory, wherein said software configures the processor to: create a pool of neural networks trained on a portion of a data set; for each of various coefficient settings for a multi-objective function: apply selective evolution subject to the multi-objective function with that coefficient setting to obtain a corresponding group of neural network ensembles; and select a local ensemble from each said group of neural network ensembles, wherein the selection is based on data not included in said portion of the data set; combine a plurality of the local ensembles to form a global ensemble; and provide a perceptible output based at least in part on a prediction by the global ensemble.