Costa, Kelton Augusto Pontara daPereira, Clayton ReginaldoPereira, Luís Augusto MartinsNakamura, Rodrigo Yuji MizobePapa, João Paulo [UNESP]2016-03-022016-03-022013Journal of Information Assurance and Security, v. 8, n. 2, p. 76-85, 2013.1554-1010http://hdl.handle.net/11449/135267In this paper we deal with the problem of boosting the Optimum-Path Forest (OPF) clustering approach using evolutionary-based optimization techniques. As the OPF classifier performs an exhaustive search to find out the size of sample's neighborhood that allows it to reach the minimum graph cut as a quality measure, we compared several optimization techniques that can obtain close graph cut values to the ones obtained by brute force. Experiments in two public datasets in the context of unsupervised network intrusion detection have showed the evolutionary optimization techniques can find suitable values for the neighborhood faster than the exhaustive search. Additionally, we have showed that it is not necessary to employ many agents for such task, since the neighborhood size is defined by discrete values, with constrain the set of possible solution to a few ones.76-85engArtigoAcesso restrito33696813960581518448107303335081903918293274719452289911668555829083697774870852