abstract = "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, we 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. We use the Pareto Evolutionary Algorithm
Adapting the Penalty (PEAP), a category of EC, selected
in this paper to deal with time consuming online
processing required by basic EC such as genetic
algorithms. Our simulation results relating to road
safety highlight the benefits of MCDM prior to such
reconfiguration. We also address the problem of
destabilization which can result from repeated
reconfigurations in response to ongoing environment
changes.",