  The travelling salesman problem (TSP) is one of the well-studied NP-hard
problems in the literature. The state-of-the art inexact TSP solvers are the
Lin-Kernighan-Helsgaun (LKH) heuristic and Edge Assembly crossover (EAX). A
recent study suggests that EAX with restart mechanisms perform well on a wide
range of TSP instances. However, this study is limited to 2,000 city problems.
We study for problems ranging from 2,000 to 85,900. We see that the performance
of the solver varies with the type of the problem. However, combining these
solvers in an ensemble setup, we are able to outperform the individual solver's
performance. We see the ensemble setup as an efficient way to make use of the
abundance of compute resources. In addition to EAX and LKH, we use several
versions of the hybrid of EAX and Mixing Genetic Algorithm (MGA). A hybrid of
MGA and EAX is known to solve some hard problems. We see that the ensemble of
the hybrid version outperforms the state-of-the-art solvers on problems larger
than 10,000 cities.
