深度学习作为旅行商问题自动算法选择的无竞争性方法
Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem
摘要：
在这项工作中，我们专注于著名的欧几里德旅行推销员问题（TSP）和两个高度竞争的不精确的启发式TSP求解器，EAX和LKH，在每实例算法选择（AS）的上下文中。我们进化了1,000个节点的实例，其中求解器显示出强烈不同的性能配置文件。这些实例作为一个探索性研究的基础上，识别良好的区分问题的特点（功能）。我们的结果概括如下：我们表明，即使（1）存在有希望的特征，（2）这些特征与文献中先前的结果一致，以及（3）用这些特征训练的模型比采用复杂的特征选择方法的模型更准确，但就惩罚平均运行时间而言，该优势并不接近虚拟最佳求解器，并且相对于单个最佳求解器的性能增益也是如此。然而，我们发现，一个无特征的深度神经网络为基础的方法完全基于视觉表示的实例已经匹配经典的AS模型的结果，从而显示出巨大的潜力，为未来的研究。
英文摘要：
In this work we focus on the well-known Euclidean Traveling Salesperson Problem (TSP) and two highly competitive inexact heuristic TSP solvers, EAX and LKH, in the context of per-instance algorithm selection (AS). We evolve instances with 1,000 nodes where the solvers show strongly different performance profiles. These instances serve as a basis for an exploratory study on the identification of well-discriminating problem characteristics (features). Our results in a nutshell: we show that even though (1) promising features exist, (2) these are in line with previous results from the literature, and (3) models trained with these features are more accurate than models adopting sophisticated feature selection methods, the advantage is not close to the virtual best solver in terms of penalized average runtime and so is the performance gain over the single best solver. However, we show that a feature-free deep neural network based approach solely based on visual representation of the instances already matches classical AS model results and thus shows huge potential for future studies.
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。
