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Erschienen in:
2008 | OriginalPaper | Buchkapitel
Determining Whether a Problem Characteristic Affects Heuristic Performance
A Rigorous Design of Experiments Approach
verfasst von : Enda Ridge, Daniel Kudenko
Erschienen in: Recent Advances in Evolutionary Computation for Combinatorial Optimization
Verlag: Springer Berlin Heidelberg
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This chapter presents a rigorous Design of Experiments (DOE) approach for determining whether a problem characteristic affects the performance of a heuristic. Specifically, it reports a study on the effect of the cost matrix standard deviation of symmetric Travelling Salesman Problem (TSP) instances on the performance of Ant Colony Optimisation (ACO) heuristics. Results demonstrate that for a given instance size, an increase in the standard deviation of the cost matrix of instances results in an increase in the difficulty of the instances. This implies that for ACO, it is insufficient to report results on problems classified only by problem size, as has been commonly done in most ACO research to date. Some description of the cost matrix distribution is also required when attempting to explain and predict the performance of these heuristics on the TSP. The study should serve as a template for similar investigations with other problems and other heuristics.