Adaptive and multilevel approach for constraint solving

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations


For many real world problems, modeled as Constraint Satisfaction Problems, there are no known efficient algorithms to solve them. The specialized literature offers a variety of solvers, which have shown satisfactory performance. Nevertheless, despite the efforts of the scientific community in developing new strategies, there is no algorithm that is the best for all possible situations. Then, several approaches have emerged to deal with the Algorithm Selection Problem. Here, we sketch the use a Choice Function for guiding a Constraint Programming solver exploiting search process features to dynamically adapt it in order to more efficiently solve Constraint Satisfaction Problems. To determine the best set of parameters of the choice function, an upper-level metaheuristic is used. The main novelty of our approach is that we reconfigure the search based solely on performance data gathered while solving the current problem.

Original languageEnglish
Title of host publicationHCI International 2013 - Posters' Extended Abstracts - International Conference, HCI International 2013, Proceedings
PublisherSpringer Verlag
Number of pages5
EditionPART I
ISBN (Print)9783642394720
StatePublished - 2013
Event15th International Conference on Human-Computer Interaction, HCI International 2013 - Las Vegas, NV, United States
Duration: 21 Jul 201326 Jul 2013

Publication series

NameCommunications in Computer and Information Science
NumberPART I
ISSN (Print)1865-0929


Conference15th International Conference on Human-Computer Interaction, HCI International 2013
Country/TerritoryUnited States
CityLas Vegas, NV


  • Algorithm selection problem
  • Autonomous search
  • Constraint satisfacion problems
  • Constraint solving


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