  This article presents the first results from using a learning classifier
system capable of performing adaptive computation with deep neural networks.
Individual classifiers within the population are composed of two neural
networks. The first acts as a gating or guarding component, which enables the
conditional computation of an associated deep neural network on a per instance
basis. Self-adaptive mutation is applied upon reproduction and prediction
networks are refined with stochastic gradient descent during lifetime learning.
The use of fully-connected and convolutional layers are evaluated on
handwritten digit recognition tasks where evolution adapts (i) the gradient
descent learning rate applied to each layer (ii) the number of units within
each layer, i.e., the number of fully-connected neurons and the number of
convolutional kernel filters (iii) the connectivity of each layer, i.e.,
whether each weight is active (iv) the weight magnitudes, enabling escape from
local optima. The system automatically reduces the number of weights and units
while maintaining performance after achieving a maximum prediction error.
