Hi, I wrote the original metric_tsp_approx in BGL a long time ago and had help from Andrew Sutton to make it 'BGL ready'. Given a bit of free time over the holidays, I became interested in making some modifications to the original (various cleanup / better test harness) and then later (after a side track with Christofides TSP heuristic, which I deemed too inefficient for its upper bound benefits) I became interested in implementing a basic LK (Lin-Kernighan) solution. To do so first requires a TSP tour as a starting point. I decided to write an implementation of the greedy Nearby Neighbors heuristic to create this initial tour, which I have done. Nearby Neighbors runs faster than the metric_tsp_approx but has more erratic solution (tour) performance. It also has some different characteristics as a local greedy solution vs. metric_tsp which creates an MST across the whole graph. Is there any interest from the group in taking this heuristic as well as the LK? The LK will perform more slowly than metric_tsp_approx and much more slowly than Nearby Neighbors but also (on average) generate better tours.