  We present an experimental validation of a recently proposed optimization
technique for reservoir computing, using an optoelectronic setup. Reservoir
computing is a robust framework for signal processing applications, and the
development of efficient optimization approaches remains a key challenge. The
technique we address leverages solely a delayed version of the input signal to
identify the optimal operational region of the reservoir, simplifying the
traditionally time-consuming task of hyperparameter tuning. We verify the
effectiveness of this approach on different benchmark tasks and reservoir
operating conditions.
