Evaluating random forest models for irace

LESLIE ANGELICA PEREZ CACERES, Bernd Bischl, Tomas Stützle

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

Automatic algorithm configurators can greatly improve the performance of algorithms by effectively searching the parameter space. As algorithm con.guration tasks can have large parameter spaces and the execution of candidate algorithm configurations is o.en very costly in terms of computation time, further improvements in the search techniques used by automatic con.gurators are important and increase the applicability of available con.guration methods. One common technique to improve the behavior of search methods when evaluations are computationally expensive are surrogate model techniques. .ese models are able to exploit the scarce available data and help to direct the search towards evaluating the most promising candidate con.gurations. In this paper, we study the use of random forests models as surrogate models in irace, a .exible automatic con.guration tool based on iterated racing that has been successfully applied in the literature. We evaluate the performance of the random forest model using di.erent se.ings when trained with data obtained from the irace con.guration process and we evaluate their performance under similar conditions as in the con.guration process. .is preliminary work aims at providing guidelines for the incorporation of random forest to the con.guration process of irace.

Original languageEnglish
Title of host publicationGECCO 2017 - Proceedings of the Genetic and Evolutionary Computation Conference Companion
PublisherAssociation for Computing Machinery, Inc
Pages1146-1153
Number of pages8
ISBN (Electronic)9781450349390
DOIs
StatePublished - 15 Jul 2017
Event2017 Genetic and Evolutionary Computation Conference Companion, GECCO 2017 - Berlin, Germany
Duration: 15 Jul 201719 Jul 2017

Publication series

NameGECCO 2017 - Proceedings of the Genetic and Evolutionary Computation Conference Companion

Conference

Conference2017 Genetic and Evolutionary Computation Conference Companion, GECCO 2017
CountryGermany
CityBerlin
Period15/07/1719/07/17

Keywords

  • Automatic algorithm configuration
  • Irace
  • Parameter tuning
  • Random forests

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