Comparing evolutionary methods for reservoir computing pre-training

Evolutionary algorithms are very efficient at finding “optimal” solutions for a variety of problems because they do not impose many limitations encountered in traditional methods. Reservoir Computing is a type of recurrent neural network that allows for the black box modeling of (nonlinear) dynamic systems. In contrast to other recurrent neural network approaches, Reservoir Computing does not train the input and internal weights of the network; only the output layer is trained. However, it is necessary to adjust parameters and topology to create a “good” reservoir for a given application. This study compares three different evolutionary methods in order to find the best reservoir applied to the task of time series forecasting. The results obtained with the methods are compared regarding the performance (prediction error) and regarding the computational complexity (time). We used three sets to compare the methods´ results. The results show that it is possible to find well-adjusted networks automatically and that the weights search, without restriction of the echo state property, allows for more adequate solutions to be found for the problem with a lower computational cost.