Denzinger, JörgBergmann, Karel2014-05-052014-06-162014-05-052014http://hdl.handle.net/11023/1504The testing of complex adaptive systems for emergent misbehaviours currently lacks any kind of automated support. In this thesis, we present Incremental Adaptive Corrective Learning, an evolutionary learning technique which automatically identifies vulnerabilities and performance-limiting behaviours induced by adversarially controlled agents inserted into a tested mobile ad-hoc network. Three case studies are presented, each demonstrating how Incremental Adaptive Corrective Learning is used to induce poor network performance in different mobile ad-hoc network applications.engUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.Artificial IntelligenceComputer ScienceArtificial Intelligencesearchevolutionary algorithmGenetic Algorithmwireless networkad-hoc networktestingMulti-agent systemsnetwork protocolSimulationadversarySecuritydoctoral thesis10.11575/PRISM/28666