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An information-based neural approach to generic constraint satisfaction

In English

Författare

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.

Publiceringsår

2002

Språk

Engelska

Sidor

1-17

Publikation/Tidskrift/Serie

Artificial Intelligence

Volym

142

Issue

1

Dokumenttyp

Artikel i tidskrift

Förlag

Elsevier

Ämne

  • Biophysics

Nyckelord

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

Status

Published

ISBN/ISSN/Övrigt

  • ISSN: 1872-7921