ISAC --Instance-Specific Algorithm Configuration

  • Authors:

  • Serdar Kadioglu;Yuri Malitsky;Meinolf Sellmann;Kevin Tierney

  • Affiliations:

  • Brown University, USA, email: {serdark,ynm,sello,ktierney}@cs.brown.edu;Brown University, USA, email: {serdark,ynm,sello,ktierney}@cs.brown.edu;Brown University, USA, email: {serdark,ynm,sello,ktierney}@cs.brown.edu;Brown University, USA, email: {serdark,ynm,sello,ktierney}@cs.brown.edu

  • Venue:
  • Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
  • Year:
  • 2010

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Abstract

We present a new method for instance-specific algorithm configuration (ISAC). It is based on the integration of the algorithm configuration system GGA and the recently proposed stochastic offline programming paradigm. ISAC is provided a solver with categorical, ordinal, and/or continuous parameters, a training benchmark set of input instances for that solver, and an algorithm that computes a feature vector that characterizes any given instance. ISAC then provides high quality parameter settings for any new input instance. Experiments on a variety of different constrained optimization and constraint satisfaction solvers show that automatic algorithm configuration vastly outperforms manual tuning. Moreover, we show that instance-specific tuning frequently leads to significant speed-ups over instance-oblivious configurations.