ISAC - Instance-Specific Algorithm Configuration

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ISAC - Instance-Specific Algorithm Configuration
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.
Serdar Kadioglu, Yuri Malitsky, Meinolf Sellmann,
Added 08 Nov 2010
Updated 08 Nov 2010
Type Conference
Year 2010
Where ECAI
Authors Serdar Kadioglu, Yuri Malitsky, Meinolf Sellmann, Kevin Tierney
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