Designing and tuning SLS through animation and graphics: An extended walk-through

Abstract

Stochastic Local Search (SLS) is quite effective for a variety of Combinatorial (Optimization) Problems. However, the performance of SLS depends on several factors and getting it right is not trivial. In practice, SLS may have to be carefully designed and tuned to give good results. Often this is done in an ad-hoc fashion. One approach to this issue is to use a tuning algorithm for finding good parameter settings to a black-box SLS algorithm. Another approach is white-box which takes advantage of the human in the process. In this paper, we show how visualization using a generic visual tool can be effective for a white-box approach to get the right SLS behavior on the fitness landscape of the problem instances at hand. We illustrate this by means of an extended walk-through on the Quadratic Assignment Problem. At the same time, we present the human-centric tool which has been developed. © Springer-Verlag Berlin Heidelberg 2007.