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A dynamically adaptive system (DAS) monitors itself and its execution environment to evaluate requirements satisfaction at run time. Unanticipated environmental conditions may produce sensory inputs that alter the self-assessment capabilities of a DAS in unpredictable and undesirable ways. Moreover, it is impossible for a human to know or enumerate all possible combinations of system and environmental...
Data diffusion techniques enable a distributed system to replicate and propagate data across a potentially unreliable network in order to provide better data protection and availability. This paper presents a novel evolutionary computation approach to developing network construction algorithms and data diffusion strategies. The proposed approach combines a linear genetic program with a cellular automaton...
Increasingly, applications need to be able to self-reconfigure in response to changing requirements and environmental conditions. Autonomic computing has been proposed as a means for automating software maintenance tasks. As the complexity of adaptive and autonomic systems grows, designing and managing the set of reconfiguration rules becomes increasingly challenging and may produce inconsistencies...
Requirements monitoring continuously probes system components and sensors to assess whether the system satisfies its requirements and to detect conditions conducive to a requirement violation. Monitoring, however, is typically computationally expensive, intrusive, and presents tradeoffs between monitoring costs and accuracy, or the degree of coverage and coherence of gathered data. As such, it may...
Evolutionary algorithms have been very successful at solving global optimization problems. Two competing goals govern the performance of evolutionary algorithms: exploration and exploitation. This paper proposes a new heuristic to keep population diversity: the shake and the regicide. The shake heuristic improves the exploration by perturbing the whole population. The regicide heuristic (kill the...
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