  For many years, Evolutionary Algorithms (EAs) have been applied to improve
Neural Networks (NNs) architectures. They have been used for solving different
problems, such as training the networks (adjusting the weights), designing
network topology, optimizing global parameters, and selecting features. Here,
we provide a systematic brief survey about applications of the EAs on the
specific domain of the recurrent NNs named Reservoir Computing (RC). At the
beginning of the 2000s, the RC paradigm appeared as a good option for employing
recurrent NNs without dealing with the inconveniences of the training
algorithms. RC models use a nonlinear dynamic system, with fixed recurrent
neural network named the \textit{reservoir}, and learning process is restricted
to adjusting a linear parametric function. %so the performance of learning is
fast and precise. However, an RC model has several hyper-parameters, therefore
EAs are helpful tools to figure out optimal RC architectures. We provide an
overview of the results on the area, discuss novel advances, and we present our
vision regarding the new trends and still open questions.
