It has long been hypothesized that operating close to the critical state is beneficial for natural, artificial and their evolutionary systems. We put this hypothesis to test in a system of evolving foraging agents controlled by neural networks that can adapt agents' dynamical regime throughout evolution. Surprisingly, we find that all populations that discover solutions, evolve to be subcritical. By a resilience analysis, we find that there are still benefits of starting the evolution in the critical regime. Namely, initially critical agents maintain their fitness level under environmental changes (for example, in the lifespan) and degrade gracefully when their genome is perturbed. At the same time, initially subcritical agents, even when evolved to the same fitness, are often inadequate to withstand the changes in the lifespan and degrade catastrophically with genetic perturbations. Furthermore, we find the optimal distance to criticality depends on the task complexity. To test it we introduce a hard and simple task: for the hard task, agents evolve closer to criticality whereas more subcritical solutions are found for the simple task. We verify that our results are independent of the selected evolutionary mechanisms by testing them on two principally different approaches: a genetic algorithm and an evolutionary strategy. In summary, our study suggests that although optimal behaviour in the simple task is obtained in a subcritical regime, initializing near criticality is important to be efficient at finding optimal solutions for new tasks of unknown complexity.

翻译：长期以来的假说认为，在接近临界态运行有利于自然系统、人工系统及其进化系统。为验证这一假说，我们构建了一个由神经网络控制的进化觅食智能体系统，该系统可在进化过程中自适应调整智能体的动态区间。令人惊讶的是，我们发现所有成功发现解决方案的种群都进化到了亚临界状态。通过韧性分析，我们发现从临界态启动进化仍具有优势：初始为临界态的智能体能够在环境变化（例如生命周期的改变）中维持其适应度水平，并在基因组受到扰动时呈现渐进退化；而初始为亚临界态的智能体，即便进化到相同适应度，往往也难以承受生命周期的变化，并在遗传扰动下发生灾难性退化。进一步研究发现，最优临界距离取决于任务复杂度。为验证这一结论，我们设计了困难任务与简单任务：在困难任务中，智能体向临界态进化更近，而在简单任务中则更多出现亚临界解。通过采用遗传算法与进化策略两种截然不同的进化机制进行验证，结果表明我们的发现与所选进化机制无关。综上所述，本研究表明：尽管简单任务的最优行为出现在亚临界区间，但在面对未知复杂度的新任务时，从临界态初始化对于高效寻找最优解至关重要。