layers, we expect a corresponding exponential increase in the number of possible
architectures. In this paper, we apply a hybrid evolutionary search procedure to define the
initialization and architectural parameters of convolutional networks, one of the first
successful deep network models. We make use of stochastic diagonal Levenberg-Marquardt
to accelerate the convergence of training, lowering the time cost of fitness evaluation. Using …