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F-Race and Iterated F-Race: An Overview

  • Mauro BirattariEmail author
  • Zhi YuanEmail author
  • Prasanna BalaprakashEmail author
  • Thomas StützleEmail author
Chapter

Abstract

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.

Keywords

Local Search Travel Salesman Problem Full Factorial Design Iterate Local Search Timetabling Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Notes

Acknowledgements

This work has been supported by META-X, an ARC project funded by the French Community of Belgium. The authors acknowledge support from the fund for scientific research FRS-FNRS of the French Community of Belgium, of which they are research associates (M.B. and T.S.), or aspirant (Z.Y.), respectively.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.IRIDIA, CoDE, Université Libre de BruxellesBrusselsBelgium

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