Anzeige
Erschienen in:
2012 | OriginalPaper | Buchkapitel
Quantifying Homogeneity of Instance Sets for Algorithm Configuration
verfasst von : Marius Schneider, Holger H. Hoos
Erschienen in: Learning and Intelligent Optimization
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Automated configuration procedures play an increasingly prominent role in realising the performance potential inherent in highly parametric solvers for a wide range of computationally challenging problems. However, these configuration procedures have difficulties when dealing with inhomogenous instance sets, where the relative difficulty of problem instances varies between configurations of the given parametric algorithm. In the literature, instance set homogeneity has been assessed using a qualitative, visual criterion based on heat maps. Here, we introduce two quantitative measures of homogeneity and empirically demonstrate these to be consistent with the earlier qualitative criterion. We also show that according to our measures, homogeneity increases when partitioning instance sets by means of clustering based on observed runtimes, and that the performance of a prominent automatic algorithm configurator increases on the resulting, more homogenous subsets.