Abstract
We propose in this paper an artificial immune system to solve CSPs. The algorithm has been designed following the framework proposed by de Castro and Timmis. We have calibrated our algorithm using Relevance Estimation and Value Calibration (REVAC), that is a new technique, recently introduced to find the parameter values for evolutionary algorithms. The tests were carried out using random generated binary constraint satisfaction problems on the transition phase where are the hardest problems. The algorithm shown to be able to find quickly good quality solutions.
Supported by Fondecyt Project 1060377.
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Zuñiga, M., Riff, MC., Montero, E. (2007). NAIS: A Calibrated Immune Inspired Algorithm to Solve Binary Constraint Satisfaction Problems. In: de Castro, L.N., Von Zuben, F.J., Knidel, H. (eds) Artificial Immune Systems. ICARIS 2007. Lecture Notes in Computer Science, vol 4628. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73922-7_3
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DOI: https://doi.org/10.1007/978-3-540-73922-7_3
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