Understanding the behaviour of heuristic search methods is a challenge. This even holds for simple local search methods such as 2-OPT for the Travelling Salesperson Problem (TSP). In this article, we present a general framework that is able to construct a diverse set of instances which are hard or easy for a given search heuristic. Such a diverse set is obtained by using an evolutionary algorithm for constructing hard or easy instances which are diverse with respect to different features of the underlying problem. Examining the constructed instance sets, we show that many combinations of two or three features give a good classification of the TSP instances in terms of whether they are hard to be solved by 2-OPT.

中文翻译：

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问题实例分类的基于特征的多样性优化

理解启发式搜索方法的行为是一个挑战。这甚至适用于简单的本地搜索方法，例如针对旅行商问题 (TSP) 的 2-OPT。在本文中，我们提出了一个通用框架，该框架能够构建一组不同的实例，这些实例对于给定的搜索启发式来说是困难的或容易的。这种多样化的集合是通过使用进化算法来构建困难或简单实例而获得的，这些实例对于潜在问题的不同特征是不同的。检查构造的实例集，我们表明两个或三个特征的许多组合对 TSP 实例是否难以通过 2-OPT 进行了很好的分类。