An information-based neural approach to generic constraint satisfaction

Henrik Jönsson, Bo Söderberg

Research output: Contribution to journalArticlepeer-review


A novel artificial neural network heuristic (INN) for general constraint satisfaction problems is presented. extending a recently suggested method restricted to boolean variables. In contrast to conventional ANN methods, it employs a particular type of non-polynomial cost function, based on the information balance between variables and constraints in a mean-field setting. Implemented as an annealing algorithm, the method is numerically explored on a testbed of Graph Coloring problems. The performance is comparable to that of dedicated heuristics, and clearly superior to that of conventional mean-field annealing.
Original languageEnglish
Pages (from-to)1-17
JournalArtificial Intelligence
Issue number1
Publication statusPublished - 2002

Subject classification (UKÄ)

  • Biophysics

Free keywords

  • constraint satisfaction
  • connectionist
  • artificial
  • neural network
  • heuristic information
  • mean-field annealing
  • graph coloring


Dive into the research topics of 'An information-based neural approach to generic constraint satisfaction'. Together they form a unique fingerprint.

Cite this