Some surprising regularities in the behaviour of stochastic local search

Abstract

This abstract gives a brief overview of our work presented in [3]. Our approach for characterising the run-time behaviour of stochastic local search (SLS) algorithms is based on a novel and adequate empirical methodology for evaluating SLS algorithms first used in [1] and presented in more detail in [2]: Instead of collecting simple statistics averaged over a large number of runs and large sets of instances, we are estimating and functionally characterising run-time distributions on single instances. The data thus obtained provides the basis for formulating hypotheses on the behaviour of SLS algorithms on problem distributions and across several domains. These hypotheses are then tested using standard statistical methodology like parameter estimation methods and goodness-of-fit tests.