# A case study of algorithm selection for the traveling thief problem

arXiv: https://arxiv.org/abs/1609.00462 · HTML: https://mlsanity.com/paper/1609.00462/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Sep 2016.

## Built on by (most cited first, 7 of 7)

- [Algorithm Selection on a Meta Level](https://mlsanity.com/paper/2107.09414/) (2021)
- [Which algorithm to select in sports timetabling?](https://mlsanity.com/paper/2309.03229/) (2023)
- [Solving Travelling Thief Problems using Coordination Based Methods](https://mlsanity.com/paper/2310.07156/) (2023)
- [Efficiently solving the thief orienteering problem with a max-min ant colony optimization approach](https://mlsanity.com/paper/2109.13103/) (2021)
- [The Traveling Thief Problem with Time Windows: Benchmarks and Heuristics](https://mlsanity.com/paper/2604.06724/) (2026)
- [Graph Neural Network-based Algorithm Selection for the Traveling Salesman Problem: A Systematic Study of Cost and Rank Losses under Distinct Budget Regimes](https://mlsanity.com/paper/2607.18632/) (2026)
- [Drive, Pack, Fly: The Travelling Thief Problem with Drone](https://mlsanity.com/paper/2608.16435/) (2026)

## Abstract (arXiv)

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.