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dc.contributor.authorPavlidis, N. G.en
dc.contributor.authorParsopoulos, K. E.en
dc.contributor.authorVrahatis, M. N.en
dc.rightsDefault Licence-
dc.subjectnash equilibriaen
dc.subjectevolutionary algorithmsen
dc.subjectparticle swarrn optimizationen
dc.subjectdifferential evolutionen
dc.subjectevolulion strategyen
dc.subjectdifferential evolutionen
dc.titleComputing Nash equilibria through computational intelligence methodsen
heal.type.enJournal articleen
heal.type.elΆρθρο Περιοδικούel
heal.identifier.primaryDOI 10.1016/
heal.recordProviderΠανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Ηλεκτρονικών Υπολογιστών και Πληροφορικήςel
heal.abstractNash equilibrium constitutes a central solution concept in game theory. The task of detecting the Nash equilibria of a finite strategic game remains a challenging problem up-to-date. This paper investigates the effectiveness of three computational intelligence techniques, namely, covariance matrix adaptation evolution strategies, particle swarm optimization, as well as. differential evolution, to compute Nash equilibria of finite strategic games. as global minima of a real-valued, nonnegative function. An issue of particular interest is to detect more than one Nash equilibria of a game. The performance of the considered computational intelligence methods on this problem is investigated using multistart and deflection. (C) 2004 Elsevier B.V. All rights reserved.en
heal.journalNameJournal of Computational and Applied Mathematicsen
heal.journalTypepeer reviewed-
Appears in Collections:Άρθρα σε επιστημονικά περιοδικά ( Ανοικτά)

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