A case study of algorithm selection for the traveling thief problem

Markus Wagner (), Marius Lindauer (), Mustafa Mısır (), Samadhi Nallaperuma () and Frank Hutter ()
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Markus Wagner: The University of Adelaide
Marius Lindauer: Albert-Ludwigs-Universität Freiburg
Mustafa Mısır: Nanjing University of Aeronautics and Astronautics
Samadhi Nallaperuma: University of Sheffield
Frank Hutter: Albert-Ludwigs-Universität Freiburg

Journal of Heuristics, 2018, vol. 24, issue 3, No 4, 295-320

Abstract: Abstract Many real-world problems are composed of several interacting components. In order to facilitate research on such interactions, the Traveling Thief Problem (TTP) was created in 2013 as the combination of two well-understood combinatorial optimization problems. With this article, we contribute in four ways. First, we create a comprehensive dataset that comprises the performance data of 21 TTP algorithms on the full original set of 9720 TTP instances. Second, we define 55 characteristics for all TPP instances that can be used to select the best algorithm on a per-instance basis. Third, we use these algorithms and features to construct the first algorithm portfolios for TTP, clearly outperforming the single best algorithm. Finally, we study which algorithms contribute most to this portfolio.

Keywords: Combinatorial optimization; Instance analysis; Algorithm portfolio (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s10732-017-9328-y

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