A general Evolutionary Framework for different classes of Critical Node Problems

@article{Aringhieri2016AGE,
  title={A general Evolutionary Framework for different classes of Critical Node Problems},
  author={Roberto Aringhieri and Andrea Grosso and Pierre Hosteins and Rosario Scatamacchia},
  journal={Eng. Appl. of AI},
  year={2016},
  volume={55},
  pages={128-145}
}
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… CONTINUE READING
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