Designing Network Topologies of Multiple Reservoir Echo State Networks: A Genetic Algorithm Based Approach

In Reservoir Computing methods, Multiple Reservoir Echo State Networks (MRESNs) with multiple reservoir encoders can have better computational abilities than the standard single Echo State Network on many time-series processing tasks. However, several hand-crafted network topologies have been widely adopted for building most existing MRESN-based models, which limits the potential of their performance of an MRESN on a given temporal processing task. In this work, we propose a new method to improve the computational ability of an MRESN based on the perspective of the neural architecture search. To accomplish this goal, we use a binary-encoding method to represent the reservoir network topology of an MRESN and develop a Genetic Algorithm (GA)-based approach to seek a desirable network topology. Experimental results show that MRESNs with the corresponding optimal network topologies searched by our proposed approach have better computational abilities than the baseline MRESN models on four benchmark time-series processing datasets.