Deep learning, powered by overparameterised Deep Neural Networks (DNNs), has seen a surge in interest in recent years. Although these networks are often pruned to a fraction of their size post-training, the Lottery Ticket Hypothesis (LTH) suggests that equally trainable, sparser subnetworks exist within them. This paper presents a new evolutionary algorithm, Neuroevolutionary Ticket Search (NeTS), which finds these efficient subnetworks in feed-forward or convolutional DNN architectures. Tested on common training datasets, NeTS can prune DNNs prior to significant gradient descent training, leading to notable performance benefits.

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© 2023 Massachusetts Institute of Technology Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license
2023
Massachusetts Institute of Technology
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Issue Section:
General Conference