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，人们假设接近临界状态对自然系统、人工系统及其进化系统是有益的。我们通过一个由神经网络控制的进化觅食智能体系统对该假设进行检验，该系统允许智能体在整个进化过程中调整其动态模式。令人惊讶的是，我们发现所有发现解决方案的种群都进化到了亚临界状态。通过弹性分析，我们发现从临界状态开始进化仍然具有优势。具体而言，初始状态为临界的智能体能够在环境变化（例如寿命变化）下维持其适应度水平，并在基因组受到扰动时性能逐渐下降。与此同时，初始状态为亚临界的智能体，即便进化到相同适应度水平，也往往难以承受寿命变化，并在基因扰动下性能灾难性下降。此外，我们发现最优临界距离取决于任务复杂度。为验证这一点，我们引入了困难任务和简单任务：在困难任务中，智能体进化到更接近临界状态，而在简单任务中则发现更多亚临界解。通过两种截然不同的方法（遗传算法和进化策略）测试，我们验证了结果与所选进化机制无关。总之，我们的研究表明，尽管在简单任务中最优行为出现在亚临界状态，但为了高效寻找未知复杂度新任务的最优解，从接近临界状态初始化至关重要。