Correspondence between neuroevolution and gradient descent

Anonymous

Keywords: neuroevolution, gradient descent, theoretical description of learning

Abstract: We show analytically that training a neural network by stochastic mutation or neuroevolution of its weights is equivalent, in the limit of small mutations, to gradient descent on the loss function in the presence of Gaussian white noise. Averaged over independent realizations of the learning process, neuroevolution is equivalent to gradient descent on the loss function. We use numerical simulation to show that this correspondence can be observed for finite mutations. Our results provide a connection between two distinct types of neural-network training, and provide justification for the empirical success of neuroevolution.

One-sentence Summary: We show analytically that training a neural network by stochastic mutation or neuroevolution of its weights is equivalent, in the limit of small mutations, to gradient descent on the loss function in the presence of Gaussian white noise.

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