  This paper presents a generic technique for improving hybrid algorithms
through the discovery of and tuning of meta-heuristics. The idea is to
represent a family of push/pull heuristics that are based upon inserting and
removing tasks in a current solution, with an algebra. We then let a learning
algorithm search for the best possible algebraic term, which represents a
hybrid algorithm for a given set of problems and an optimization criterion. In
a previous paper, we described this algebra in detail and provided a set of
preliminary results demonstrating the utility of this approach, using vehicle
routing with time windows (VRPTW) as a domain example. In this paper we expand
upon our results providing a more robust experimental framework and learning
algorithms, and report on some new results using the standard Solomon
benchmarks. In particular, we show that our learning algorithm is able to
achieve results similar to the best-published algorithms using only a fraction
of the CPU time. We also show that the automatic tuning of the best hybrid
combination of such techniques yields a better solution than hand tuning, with
considerably less effort.
