  This article introduces Random Error Sampling-based Neuroevolution (RESN), a
novel automatic method to optimize recurrent neural network architectures. RESN
combines an evolutionary algorithm with a training-free evaluation approach.
The results show that RESN achieves state-of-the-art error performance while
reducing by half the computational time.
