A graph-based immune-inspired constraint satisfaction search

María Cristina Riff, Marcos Zúñiga, Elizabeth Montero

Resultado de la investigación: Contribución a una revistaArtículo

10 Citas (Scopus)

Resumen

We propose an artificial immune algorithm to solve constraint satisfaction problems (CSPs). Recently, bio-inspired algorithms have been proposed to solve CSPs. They have shown to be efficient in solving hard problem instances. Given that recent publications indicate that immune-inspired algorithms offer advantages to solve complex problems, our main goal is to propose an efficient immune algorithm which can solve CSPs. We have calibrated our algorithm using relevance estimation and value calibration (REVAC), which is a new technique recently introduced to find the parameter values for evolutionary algorithms. The tests were carried out using randomly generated binary constraint satisfaction problems and instances of the three-colouring problem with different constraint networks. The results suggest that the technique may be successfully applied to solve CSPs.

Idioma originalInglés
Páginas (desde-hasta)1133-1142
Número de páginas10
PublicaciónNeural Computing and Applications
Volumen19
N.º8
DOI
EstadoPublicada - 30 jun 2010

Áreas temáticas de ASJC Scopus

  • Inteligencia artificial
  • Software

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