par Birattari, Mauro 
;Yuan, Zhi ;Balaprakash, Prasanna ;Stützle, Thomas
Editeur scientifique Bartz-Beielstein, T.;Chiarandini, Marco;Paquete, Luis;Preuss, M.
Référence Empirical Methods for the Analysis of Optimization Algorithms, Springer, Berlin, Germany, page (311-336)
Publication Publié, 2010
;Yuan, Zhi ;Balaprakash, Prasanna ;Stützle, Thomas Editeur scientifique Bartz-Beielstein, T.;Chiarandini, Marco;Paquete, Luis;Preuss, M.
Référence Empirical Methods for the Analysis of Optimization Algorithms, Springer, Berlin, Germany, page (311-336)
Publication Publié, 2010
Partie d'ouvrage collectif
| Résumé : | 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. © 2010 Springer-Verlag Berlin Heidelberg. |
