Combining multiple heuristics

Abstract

In this work we introduce and study the question of combining multiple heuristics. Given a problem instance, each of the multiple heuristics is capable of computing the correct solution, but has a different cost. In our models the user executes multiple heuristics until one of them terminates with a solution. Given a set of problem instances, we show how to efficiently compute an optimal fixed schedule for a constant number of heuristics, and show that in general, the problem is computationally hard even to approximate (to within a constant factor). We also discuss a probabilistic configuration, in which the problem instances are drawn from some unknown fixed distribution, and show how to compute a near optimal schedule for this setup. © Springer-Verlag Berlin Heidelberg 2006.