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CP
2010
Springer

Ensemble Classification for Constraint Solver Configuration

13 years 1 months ago
Ensemble Classification for Constraint Solver Configuration
The automatic tuning of the parameters of algorithms and automatic selection of algorithms has received a lot of attention recently. One possible approach is the use of machine learning techniques to learn classifiers which, given the characteristics of a particular problem, make a decision as to which algorithm or what parameters to use. Little research has been done into which machine learning algorithms are suitable and the impact of picking the "right" over the "wrong" technique. This paper investigates the differences in performance of several techniques on different data sets. It furthermore provides evidence that by using a meta-technique which combines several machine learning algorithms, we can avoid the problem of having to pick the "best" one and still achieve good performance.
Lars Kotthoff, Ian Miguel, Peter Nightingale
Added 10 Feb 2011
Updated 10 Feb 2011
Type Journal
Year 2010
Where CP
Authors Lars Kotthoff, Ian Miguel, Peter Nightingale
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