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F-Race and iterated F-Race: an overview. (English) Zbl 1204.68280
Bartz-Beielstein, Thomas (ed.) et al., Experimental methods for the analysis of optimization algorithms. With forewords by Catherine C. McGeoch and Hans Paul Schwefel. Berlin: Springer (ISBN 978-3-642-02537-2/hbk; 978-3-642-02538-9/ebook). 311-336 (2010).
Summary: Algorithms for solving hard optimization problems typically have several parameters that need to be set appropriately such that some aspect of performance is optimized. In this chapter, we review F-Race, a racing algorithm for the task of automatic algorithm configuration. F-Race is based on a statistical approach for selecting the best configuration out of a set of candidate configurations under stochastic evaluations. We review the ideas underlying this technique and discuss an extension of the initial F-Race algorithm, which leads to a family of algorithms that we call iterated F-Race. Experimental results comparing one specific implementation of iterated F-Race to the original F-Race algorithm confirm the potential of this family of algorithms.
For the entire collection see [Zbl 1203.68003].

68W40 Analysis of algorithms
68W05 Nonnumerical algorithms
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