论文标题
通过自然计算来扩展自选模型
Scaling up the self-optimization model by means of on-the-fly computation of weights
论文作者
论文摘要
自我优化（SO）模型是一种有用的计算模型，用于研究“软”人工生命中的自组织，因为它已被证明足以模拟各种复杂的自适应系统。到目前为止，现有的工作已经在相对较小的网络大小上完成，排除了对新现象的研究，这可能是由于互连网络中大量的节点引起的复杂性而产生的。这项工作介绍了SO模型的新颖实现，该模型将其缩放为$ \ Mathcal {O} \ left（N^{2} \ right）$，相对于节点$ n $的数量，并演示了SO模型对具有系统尺寸的网络的适用性，该网络的大小比以前的研究高了几个级数。消除幼稚的$ \ MATHCAL {O} \ left（n^{3} \ right）$算法的过度计算成本，我们的直通计算铺平了研究实质上更大的系统尺寸的方式，从而使未来的研究中有更多的变化和复杂性。
The Self-Optimization (SO) model is a useful computational model for investigating self-organization in "soft" Artificial life (ALife) as it has been shown to be general enough to model various complex adaptive systems. So far, existing work has been done on relatively small network sizes, precluding the investigation of novel phenomena that might emerge from the complexity arising from large numbers of nodes interacting in interconnected networks. This work introduces a novel implementation of the SO model that scales as $\mathcal{O}\left(N^{2}\right)$ with respect to the number of nodes $N$, and demonstrates the applicability of the SO model to networks with system sizes several orders of magnitude higher than previously was investigated. Removing the prohibitive computational cost of the naive $\mathcal{O}\left(N^{3}\right)$ algorithm, our on-the-fly computation paves the way for investigating substantially larger system sizes, allowing for more variety and complexity in future studies.
