Configuring irace using surrogate configuration benchmarks

Nguyen Dang, Leslie Pérez Cáceres, Patrick De Causmaecker, Thomas Stützle

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)


Over the recent years, several tools for the automated configuration of parameterized algorithms have been developed. These tools, also called configurators, have themselves parameters that influence their search behavior and make them malleable to different kinds of configuration tasks. The default values of these parameters are set manually based on the experience of the configurator's developers. Studying the impact of these parameters or configuring them is very expensive as it would require many executions of these tools on configuration tasks, each taking often many hours or days of computation. In this work, we tackle this problem using a meta-tuning process, based on the use of surrogate benchmarks that are much faster to evaluate. This paper studies the feasibility of this process using the popular irace configurator as the method to be meta-configured. We first study the consistency between the real and surrogate benchmarks using three measures: the prediction accuracy of the surrogate models, the homogeneity of the benchmarks and the list of important algorithm parameters. Afterwards, we use irace to configure irace on those surrogates. Experimental results indicate the feasibility of this process and a clear potential improvement of irace over its default configuration.
Original languageEnglish
Title of host publicationGECCO '17
Subtitle of host publicationProceedings of the Genetic and Evolutionary Computation Conference
Place of PublicationNew York
Number of pages8
ISBN (Print)9781450349208
Publication statusPublished - Jul 2017
EventThe Genetic and Evolutionary Computation Conference 2017 - Berlin, Germany
Duration: 15 Jul 201719 Jul 2017


ConferenceThe Genetic and Evolutionary Computation Conference 2017
Abbreviated titleGECCO 2017
Internet address


  • Automatic algorithm configuration
  • Surrogate benchmarks


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