Local Search and the Traveling Salesman Problem: A Feature-Based Characterization of Problem Hardness

By Olaf Mersmann, Bernd Bischl, Jakob Bossek, Heike Trautmann, Markus Wagner, F. Neumann. 2012

55 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Industrial and Manufacturing Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Quoc V. Le
2. Yoshua Bengio
3. Mohit Bansal
4. Mohammad Norouzi
5. Jianfeng Gao
6. Sergey Levine
7. Graham Neubig
8. Caiming Xiong

In Computational Theory and Mathematics

1. Quoc V. Le
2. Pascal Kerschke
3. Heike Trautmann
4. F. Neumann
5. Rui Wang
6. Mohammad Norouzi
7. Tao Zhang
8. Samy Bengio

In Industrial and Manufacturing Engineering

1. Rui Wang
2. Hieu Pham
3. F. Neumann
4. Mohammad Norouzi
5. Tao Zhang
6. Samy Bengio
7. Holger H. Hoos
8. Quoc V. Le

Citations in the corpus

• Neural Combinatorial Optimization with Reinforcement Learning (2016, methodology): “OR-Tools improves over Christofides’ solutions with simple local search operators, including 2-opt (Johnson, 1990) and a version of the Lin-Kernighan heuristic (Lin & Kernighan, 1973), stopping when it reaches a local minimum.”
• Deep Reinforcement Learning for Multiobjective Optimization (2019, methodology): “In addition, several handcrafted heuristics especially designed for TSP have been studied, such as the Lin– Kernighan heuristic [6] and the 2-opt local search [7].”
• Automated Algorithm Selection: Survey and Perspectives (2018, methodology): “…an evolutionary algorithm dates back to van Hemert (2006) and Smith-Miles et al. (2010), who constructed instances that are extremely hard or easy for specific TSP solvers; these works were followed by more sophisticated approaches of Mersmann et al. (2012, 2013) and Nallaperuma et al. (2013).”