Online Heuristic Selection in Constraint Programming

Abstract : This paper presents our first attempt to apply Support Vector Machines to the problem of automatically tuning CP search algorithms. More precisely, we exploit instances features to dynamically adapt the search strategy of a CP solver in order to more efficiently solve a given instance. In these preliminary results, adaptation is restricted to restart points, and the number of times the strategy changes is also restricted. We report very encouraging results where our adaptation outperforms what is currently considered as one of the state of the art dynamic variable selection strategy.
Document type :
Other publications
Complete list of metadatas

Cited literature [24 references]  Display  Hide  Download

https://hal.inria.fr/inria-00392752
Contributor : Alejandro Arbelaez <>
Submitted on : Monday, June 8, 2009 - 10:30:02 PM
Last modification on : Tuesday, June 9, 2009 - 8:23:21 AM
Long-term archiving on: Monday, October 15, 2012 - 12:05:58 PM

File

search-socs.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : inria-00392752, version 1

Collections

Citation

Alejandro Arbelaez, Youssef Hamadi, Michèle Sebag. Online Heuristic Selection in Constraint Programming. 2009. ⟨inria-00392752⟩

Share

Metrics

Record views

545

Files downloads

399