Abstract
In this keynote paper we present an Immune Algorithm based on the Clonal Selection Principle to explore the combinatorial optimization capability. We consider only two immunological entities, antigens and B cells, three parameters, and the cloning, hypermutation and aging immune operators. The experimental results shows how these immune operators couple the clonal expansion dynamics are sufficient to obtain optimal solutions for graph coloring problem, minimum hitting set problem and satisfiability hard instances, and that the IA designed is very competitive with the best evolutionary algorithms.
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Cutello, V., Nicosia, G. (2006). A Clonal Selection Algorithm for Coloring, Hitting Set and Satisfiability Problems. In: Apolloni, B., Marinaro, M., Nicosia, G., Tagliaferri, R. (eds) Neural Nets. WIRN NAIS 2005 2005. Lecture Notes in Computer Science, vol 3931. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11731177_39
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DOI: https://doi.org/10.1007/11731177_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33183-4
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