Abstract
It is obvious that, given a problem instance, some heuristic algorithms can perform vastly better than others; however, in most cases the existing literature provides little guidance for choosing the best heuristic algorithm. This paper describes how runtime performance predictors can be used to identify a good algorithm for a particular problem instance. The approach is demonstrated on two families of heuristic algorithms.
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© 1996 Springer-Verlag Berlin Heidelberg
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Allen, J.A., Minton, S. (1996). Selecting the right heuristic algorithm: Runtime performance predictors. In: McCalla, G. (eds) Advances in Artifical Intelligence. Canadian AI 1996. Lecture Notes in Computer Science, vol 1081. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61291-2_40
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DOI: https://doi.org/10.1007/3-540-61291-2_40
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