Fitness Landscape based Parameter Estimation for Robust Taboo Search
A. Beham, E. Pitzer, M. Affenzeller - Fitness Landscape based Parameter Estimation for Robust Taboo Search - Computer Aided Systems Theory (Eurocast 2013), Las Palmas, Spain, 2013, pp. 292-299
Metaheuristic optimization algorithms are general optimization strategies suited to solve a range of real-world relevant optimization problems. Many metaheuristics expose parameters that allow to tune the effort that these algorithms are allowed to make and also the strategy and search behavior. Adjusting these parameters allows to increase the algorithms' performances with respect to different problem- and problem instance characteristics. The difficulty in exposing parameters of metaheuristics is that these parameters need to be set and should be adjusted for good performance. Also, choosing a reasonable default value is not an easy task for algorithm developers. The purpose of this work is, on the one hand to explore the effect of parameter settings and provide more suited default values, and on the other hand to introduce a new method to use fitness landscape analysis (FLA) for the prediction of algorithm parameterization.