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Intelligent Transportation Systems (ITS) are increasingly important since they aim to bring solutions to crucial problems related to transportation networks such as congestion and various road incidents. Management of ITS, as other complex and distributed applications, has to cope with unforeseeable events and incomplete data while guaranteeing a Quality of Service (QoS) defined by multiple criteria reflecting real-life needs. To enable applications to adapt to changing environments, the authors define a methodology of dynamic architecture reconfiguration based on Multi-Criteria Decision Making (MCDM) using Evolutionary Computing (EC) to find the best combination of architecture components.
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