A case study of algorithm selection for the traveling thief problem
This work provides a method for selecting optimal algorithms for TTP instances, which is incremental as it builds on existing TTP research.
The authors tackled the problem of algorithm selection for the Traveling Thief Problem (TTP) by creating a comprehensive dataset of 21 algorithms on 9720 instances and defining 55 instance characteristics, resulting in the first algorithm portfolios that clearly outperform the single best algorithm.
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.