  It has long been hypothesized that operating close to the critical state is
beneficial for natural and artificial systems. We test this hypothesis by
evolving foraging agents controlled by neural networks that can change the
system's dynamical regime throughout evolution. Surprisingly, we find that all
populations, regardless of their initial regime, evolve to be subcritical in
simple tasks and even strongly subcritical populations can reach comparable
performance. We hypothesize that the moderately subcritical regime combines the
benefits of generalizability and adaptability brought by closeness to
criticality with the stability of the dynamics characteristic for subcritical
systems. By a resilience analysis, we find that initially critical agents
maintain their fitness level even under environmental changes and degrade
slowly with increasing perturbation strength. On the other hand, subcritical
agents originally evolved to the same fitness, were often rendered utterly
inadequate and degraded faster. We conclude that although the subcritical
regime is preferable for a simple task, the optimal deviation from criticality
depends on the task difficulty: for harder tasks, agents evolve closer to
criticality. Furthermore, subcritical populations cannot find the path to
decrease their distance to criticality. In summary, our study suggests that
initializing models near criticality is important to find an optimal and
flexible solution.

Neural and Evolutionary Computing;Disordered Systems and Neural Networks;Multiagent Systems;Adaptation and Self-Organizing Systems;Populations and Evolution