用张量网络替代遗传算法交叉操作，提升优化性能。
Tensor Network Estimation of Distribution Algorithms
- 将张量网络作为生成模型替代传统遗传算法的交叉操作。
- 生成模型拟合数据能力越强，优化效果不一定越好。
- 在生成结果后增加突变操作，显著提升优化表现。
张量网络最初用于多体量子物理，在计算科学中应用广泛，涵盖数值方法与机器学习。近期研究将张量网络融入进化优化算法，本质上是用张量网络生成模型替代遗传算法的传统交叉操作。本文从估计分布算法（EDAs）视角分析此类方法，发现优化性能与生成模型能力之间并无直接关联：生成模型对训练数据分布的拟合越优，其参与的优化算法性能未必更佳。这引发如何有效整合强大生成模型到优化流程中的问题。为此，我们发现，在生成模型输出后加入显式突变操作，通常能显著改善优化表现。
原文摘要 · Abstract (English)
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.
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