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