A graph-based immune-inspired constraint satisfaction search

María Cristina Riff, Marcos Zúñiga, Elizabeth Montero

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)1133-1142
Number of pages10
JournalNeural Computing and Applications
Volume19
Issue number8
DOIs
Publication statusPublished - 30 Jun 2010

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software

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