  Neural network structures have a critical impact on the accuracy and
stability of forecasting. Neural architecture search procedures help design an
optimal neural network according to some loss function, which represents a set
of quality criteria. This paper investigates the problem of neural network
structure optimization. It proposes a way to construct a loss function, which
contains a set of additive elements. Each element is called the regularizer. It
corresponds to some part of the neural network structure and represents a
criterion to optimize. The optimization procedure changes the structure in
iterations. To optimize various parts of the structure, the procedure changes
the set of regularizers according to some schedule. The authors propose a way
to construct the additive regularization schedule. By comparing regularized
models with non-regularized ones for a collection of datasets the computational
experiments show that the proposed method finds efficient neural network
structure and delivers accurate networks of low complexity.
