We design a flexible Evolutionary Framework for solving several classes of the Critical Node Problem (CNP), i.e. the maximal fragmentation of a graph through node deletion, given a measure of connectivity. The algorithm uses greedy rules in order to lead the search towards good quality solutions during reproduction and mutation phases. Such rules, which are only partially reported in the literature, are generalised and adapted to the six different formulations of the CNP considered along the paper. The link between solutions of different CNP formulations is investigated, both quantitatively and qualitatively. Furthermore, we provide a comparison with best known results when those are available in literature that confirms the good overall quality of our solutions.

A General Evolutionary Framework for different classes of Critical Node Problems

ARINGHIERI, ROBERTO;GROSSO, Andrea Cesare;HOSTEINS, Pierre;
2016

Abstract

We design a flexible Evolutionary Framework for solving several classes of the Critical Node Problem (CNP), i.e. the maximal fragmentation of a graph through node deletion, given a measure of connectivity. The algorithm uses greedy rules in order to lead the search towards good quality solutions during reproduction and mutation phases. Such rules, which are only partially reported in the literature, are generalised and adapted to the six different formulations of the CNP considered along the paper. The link between solutions of different CNP formulations is investigated, both quantitatively and qualitatively. Furthermore, we provide a comparison with best known results when those are available in literature that confirms the good overall quality of our solutions.
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http://www.sciencedirect.com/science/article/pii/S0952197616301191
Evolutionary algorithm, Critical Node Problem, Graph fragmentation, Greedy rules, Connectivity measures
Aringhieri, Roberto; Grosso, Andrea; Hosteins, Pierre; Scatamacchia, Rosario
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2318/1583153
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