Structure optimization of dynamic reservoir ensemble using genetic algorithm

Reservoir computing has been widely applied in dynamical system modeling and solving time-dependent problems at low computational expense. However, when confronting some complex tasks that exhibit multiple sets of dynamics, the conventional reservoir computing model with a single reservoir may become ineffective and powerless. Inspired by the modality-independent but functionally connected brain regions, the concept of reservoir ensemble has been proposed which contains multiple reservoirs. In this paper, we propose a new dynamic reservoir ensemble model which is capable of automatically adapting and optimizing the synaptic and structural plasticity of a reservoir ensemble towards an optimal performance using the genetic algorithm. As shown in a real-life time series application - temperature prediction, the proposed model demonstrates superior performance over both the conventional single-reservoir model and the static reservoir ensemble model.