  Recurrent neural networks (RNNs) are a powerful approach for time series
prediction. However, their performance is strongly affected by their
architecture and hyperparameter settings. The architecture optimization of RNNs
is a time-consuming task, where the search space is typically a mixture of
real, integer and categorical values. To allow for shrinking and expanding the
size of the network, the representation of architectures often has a variable
length. In this paper, we propose to tackle the architecture optimization
problem with a variant of the Bayesian Optimization (BO) algorithm. To reduce
the evaluation time of candidate architectures the Mean Absolute Error Random
Sampling (MRS), a training-free method to estimate the network performance, is
adopted as the objective function for BO. Also, we propose three fixed-length
encoding schemes to cope with the variable-length architecture representation.
The result is a new perspective on accurate and efficient design of RNNs, that
we validate on three problems. Our findings show that 1) the BO algorithm can
explore different network architectures using the proposed encoding schemes and
successfully designs well-performing architectures, and 2) the optimization
time is significantly reduced by using MRS, without compromising the
performance as compared to the architectures obtained from the actual training
procedure.
