Stochastic Reverse Hillclimbing and Iterated Local Search

This paper analyzes the detection of stagnation states in iterated local search algorithms. This is done considering elements such as the population size, the length of the encoding and the number of observed non-improving iterations. This analysis isolates the features of the target problem within one parameter for which three different estimations are given: two static a priori estimations and a dynamic approach. In the latter case, a stochastic reverse hillclimbing algorithm is used to extract information from the fitness landscape. The applicability of these estimations is studied and exemplified on different problems.