|کد مقاله||کد نشریه||سال انتشار||مقاله انگلیسی||ترجمه فارسی||نسخه تمام متن|
|380158||1437423||2016||18 صفحه PDF||سفارش دهید||دانلود رایگان|
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.
Journal: Engineering Applications of Artificial Intelligence - Volume 55, October 2016, Pages 128–145