Neural Computing and Applications

, Volume 19, Issue 8, pp 1133–1142 | Cite as

A graph-based immune-inspired constraint satisfaction search

  • María-Cristina Riff
  • Marcos Zúñiga
  • Elizabeth Montero


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.


Evolutionary Algorithm Constraint Satisfaction Problem Artificial Immune System Immune Network Immune Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag London Limited 2010

Authors and Affiliations

  • María-Cristina Riff
    • 1
  • Marcos Zúñiga
    • 1
  • Elizabeth Montero
    • 1
  1. 1.Department of Computer ScienceTechnical University Federico Santa MaríaValparaísoChile

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