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A novel feature-based approach to characterize algorithm performance for the traveling salesperson problem

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Abstract

Meta-heuristics are frequently used to tackle NP-hard combinatorial optimization problems. With this paper we contribute to the understanding of the success of 2-opt based local search algorithms for solving the traveling salesperson problem (TSP). Although 2-opt is widely used in practice, it is hard to understand its success from a theoretical perspective. We take a statistical approach and examine the features of TSP instances that make the problem either hard or easy to solve. As a measure of problem difficulty for 2-opt we use the approximation ratio that it achieves on a given instance. Our investigations point out important features that make TSP instances hard or easy to be approximated by 2-opt.

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Correspondence to Heike Trautmann.

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The conference version of this article appeared in the proceedings of the Learning and Intelligent Optimization Conference (LION) 2012 [25].

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Mersmann, O., Bischl, B., Trautmann, H. et al. A novel feature-based approach to characterize algorithm performance for the traveling salesperson problem. Ann Math Artif Intell 69, 151–182 (2013). https://doi.org/10.1007/s10472-013-9341-2

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