Multiobjective coevolution of deep neural network architectures
AssigneeCognizant Technology Solutions U.S. Corporation
InventorsJason Zhi Liang, Elliot Meyerson, Risto Miikkulainen
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Abstract
An evolutionary AutoML framework called LEAF optimizes hyperparameters, network architectures and the size of the network. LEAF makes use of both evolutionary algorithms (EAs) and distributed computing frameworks. A multiobjective evolutionary algorithm is used to maximize the performance and minimize the complexity of the evolved networks simultaneously by calculating the Pareto front given a group of individuals that have been evaluated for multiple objectives.
Claims6
A method for co-evolution of deep neural network (DNN) hyperparameters and architecture in accordance with multiple objective optimization, the method comprising:
- evaluating assembled DNNs in an initial population in accordance with a primary and secondary objective, wherein each assembled DNN in the initial population is comprised of modules and is assembled in accordance with an architecture blueprint, and further wherein the evaluation results in selection of a first group of selected assembled DNNs and a second group of leftover assembled DNNs;
- for each subsequent generation of an evolution run:
- creating an archive of assembled DNNs from the second group of leftover assembled DNNs from a last generation;
- evaluating each assembled DNN in the archive based on each of the primary and secondary objectives;
- determining a Pareto front of assembled DNN in the archive;
- removing assembled DNNs in the Pareto front and adding the removed DNNs to a new archive;
- and
- truncating the new archive by removing a last fraction of added assembled DNNs;
- wherein the method for co-evolution ends when values for at least the primary objective converge.
Dependencies are inferred from the claim text.