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A graph-based immune-inspired constraint satisfaction search

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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.

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Notes

  1. http://www.web.cs.ualberta.ca/joe/.

  2. http://www.sop.inria.fr/orion/personnel/Marcos.Zuniga/CSPsolver.zip.

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Authors and Affiliations

  1. Department of Computer Science, Technical University Federico Santa María, Valparaíso, Chile

    María-Cristina Riff, Marcos Zúñiga & Elizabeth Montero

Authors
  1. María-Cristina Riff
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  2. Marcos Zúñiga
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  3. Elizabeth Montero
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Corresponding author

Correspondence to María-Cristina Riff.

Additional information

Marcos Zúñiga has been financed as Scientific Assistant DGIP, UTFSM.

Supported by Fondecyt Project 1080110.

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Riff, MC., Zúñiga, M. & Montero, E. A graph-based immune-inspired constraint satisfaction search. Neural Comput & Applic 19, 1133–1142 (2010). https://doi.org/10.1007/s00521-010-0390-8

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  • DOI: https://doi.org/10.1007/s00521-010-0390-8

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