Abstract

Reservoir computing originally relies on a random, fixed population of recurrently connected neurons, but in practice there is a need for algorithms that tailor this reservoir to problem classes. We propose an evolutionary algorithm to adapt size and topology of the network as well as synaptic strengths of connections within the reservoir. Experiments show that the evolved networks significantly outperform standard architectures in terms of prediction performance and compare well with the results of alternative neuroevolution approaches. For applications in which both space and execution time complexity are important, we present an evolutionary multi-objective algorithm addressing the trade-off between identifying dynamical systems with maximum accuracy and minimizing reservoir complexity. This vector optimization algorithm utilizes the contributed hypervolume for selection and for online adaptation of operator probabilities, which considerably improves the search process. Analysis of the evolved networks reveals insights about task-dependent macroscopic properties of the adapted reservoirs