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An information-based neural approach to generic constraint satisfaction

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Author

Summary, in English

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.

Publishing year

2002

Language

English

Pages

1-17

Publication/Series

Artificial Intelligence

Volume

142

Issue

1

Document type

Journal article

Publisher

Elsevier

Topic

  • Biophysics

Keywords

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

Status

Published

ISBN/ISSN/Other

  • ISSN: 1872-7921