Evolutionary Optimization Reveals Structural Constraints on Reservoir Architecture for Spatiotemporal Chaos

The study presents an evolutionary optimization approach to reservoir computing, focusing on five construction hyperparameters: size, connectivity degree, spectral radius, input scaling, and readout regularization. Using the Kuramoto–Sivashinsky equation for spatiotemporal chaos, the evolved reservoirs demonstrated reduced prediction error, an extended forecast horizon, and a refined cost-modularity relationship, indicating that structural constraints emerge from evolutionary processes. This research highlights the potential of evolutionary reservoir computing as a bio-inspired framework for understanding how predictive tasks influence the architecture of adaptive dynamical networks, which is relevant for practitioners aiming to enhance the efficiency and accuracy of AI models.