Genetic algorithm for reservoir computing optimization
Aida Araújo Ferreira, Teresa B. Ludermir · 2009
This paper presents reservoir computing optimization using genetic algorithm. Reservoir computing is a new paradigm for using artificial neural networks. Despite its promising performance, Reservoir Computing has still some drawbacks: the reservoir is created randomly; the reservoir needs to be large enough to be able to capture all the features of the data. We propose here a method to optimize the choice of global parameters using genetic algorithm. This method was applied on a real problem of time series forecasting. The time of search for the best global parameters with GA was just 22.22% of the time- consuming task to an exhausting search of the same parameters.