Understanding the behaviour of heuristic search methods is a challenge. This even holds for simple local search methods such as 2-OPT for the Traveling Salesperson problem. In this paper, we present a general framework that is able to construct a diverse set of instances that 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 that 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.
@article{arxiv.1510.08568,
title = {Feature-Based Diversity Optimization for Problem Instance Classification},
author = {Wanru Gao and Samadhi Nallaperuma and Frank Neumann},
journal= {arXiv preprint arXiv:1510.08568},
year = {2020}
}