一个深度学习引导的图着色问题模因框架
A deep learning guided memetic framework for graph coloring problems
摘要：
给定一个无向图$G=(V,E)$，它有一个顶点集$V$和一个边集$E$，图着色问题涉及到将顶点划分为不同的独立集。在本文中，我们提出了一个新的框架，它将深度神经网络与经典元分析的最佳工具相结合，用于图着色。所提出的方法进行评估两个流行的图着色问题（顶点着色和加权着色）。著名的基准图上的计算实验表明，所提出的方法是能够获得高度竞争力的结果，这两个问题。对深度学习在该方法中的贡献的研究强调，可以学习相关模式，这些模式有助于获得图着色问题的更好解决方案。
英文摘要：
Given an undirected graph $G=(V,E)$ with a set of vertices $V$ and a set of edges $E$, a graph coloring problem involves finding a partition of the vertices into different independent sets. In this paper we present a new framework that combines a deep neural network with the best tools of classical metaheuristics for graph coloring. The proposed method is evaluated on two popular graph coloring problems (vertex coloring and weighted coloring). Computational experiments on well-known benchmark graphs show that the proposed approach is able to obtain highly competitive results for both problems. A study of the contribution of deep learning in the method highlights that it is possible to learn relevant patterns useful to obtain better solutions to graph coloring problems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
