标题： 张量网络分布估计算法 显示英文标题

标题： Tensor Network Estimation of Distribution Algorithms

摘要： 张量网络是一种最初在多体量子物理背景下使用的工具，现在在计算科学中有广泛的应用，从数值方法到机器学习。 将张量网络整合到进化优化算法中的方法最近出现在文献中。 本质上，这些方法可以理解为用基于张量网络的生成模型取代遗传算法的传统交叉操作。 我们从这些方法是分布估计算法（EDAs）的角度进行研究。 我们发现这些方法的优化性能与生成模型的能力之间并没有直接的关系。 在生成模型更好地（即它们更好地建模了其训练数据所来自的分布）的情况下，不一定能导致它们所组成的优化算法性能更好。 这引发了如何最好地将强大的生成模型融入优化过程的问题。 鉴于此，我们发现向生成模型的输出添加一个显式的突变算子通常能提高优化性能。 显示英文摘要

摘要： Tensor networks are a tool first employed in the context of many-body quantum physics that now have a wide range of uses across the computational sciences, from numerical methods to machine learning. Methods integrating tensor networks into evolutionary optimization algorithms have appeared in the recent literature. In essence, these methods can be understood as replacing the traditional crossover operation of a genetic algorithm with a tensor network-based generative model. We investigate these methods from the point of view that they are Estimation of Distribution Algorithms (EDAs). We find that optimization performance of these methods is not related to the power of the generative model in a straightforward way. Generative models that are better (in the sense that they better model the distribution from which their training data is drawn) do not necessarily result in better performance of the optimization algorithm they form a part of. This raises the question of how best to incorporate powerful generative models into optimization routines. In light of this we find that adding an explicit mutation operator to the output of the generative model often improves optimization performance.