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In this paper, we introduce an alternative approach, namely GEN (GeneticEvolution Network) Model, to the deep learning models. Instead of building onesingle deep model, GEN adopts a genetic-evolutionary learning strategy to builda group of unit models generations by generations. Significantly different fromthe wellknown representation learning models with extremely deep structures,the unit models covered in GEN are of a much shallower architecture. In thetraining process, from each generation, a subset of unit models will beselected based on their performance to evolve and generate the child models inthe next generation. GEN has significant advantages compared with existing deeprepresentation learning models in terms of both learning effectiveness,efficiency and interpretability of the learning process and learned results.Extensive experiments have been done on diverse benchmark datasets, and theexperimental results have demonstrated the outstanding performance of GENcompared with the state-of-the-art baseline methods in both effectiveness ofefficiency.

GEN Model: An Alternative Approach to Deep Neural Network Models