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，有假说认为在接近临界状态时运行对自然系统、人工系统及其进化系统有益。我们将这一假说放在一个由神经网络控制的进化觅食智能体系统中进行检验，该网络能使智能体在整个进化过程中调整自身动态区间。令人惊讶的是，我们发现所有找到解决方案的种群最终都进化至亚临界状态。通过韧性分析，我们发现从临界状态启动进化仍有其优势：即初始处于临界状态的智能体能在环境变化（例如生命周期变化）中维持其适应度水平，并在基因组受到扰动时呈现性能的优雅退化；而初始处于亚临界状态的智能体，即使进化到相同适应度水平，往往无法承受生命周期变化，并在基因扰动下出现灾难性退化。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，我们引入了一项困难任务和一项简单任务：在困难任务中，智能体进化得更接近临界状态，而简单任务则发现了更多的亚临界解决方案。我们通过两种根本不同的方法——遗传算法和进化策略——进行测试，验证了结果与所选进化机制无关。总之，我们的研究表明：尽管简单任务的最优行为出现在亚临界区间，但初始化于临界状态对于高效探索未知复杂度新任务的最优解至关重要。