Genetic algorithms (GA) are a class of powerful metaheuristic search methods that solve complex and highly nonlinear problems. However, reuse opportunities have been underexploited because reuse was made at the code level. We argue that this is inefficient because it is complex and error prone. At the opposite, we propose the use of Software Product Lines engineering (SPLE) because it offers an effective way to easily manage commonalities at the model level and efficiently customize and derive a relevant product from a family of products. Another important feature of our approach is that it opens the door to the exploitation of dynamic Software product line techniques for dynamically evolving a genetic algorithm during execution.

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Increasingly, autonomic systems are present in our lives. For this kind of systems the ability to self-reconfigure and adapt in response to changes in users requirements and environmental conditions is primordial. Several approaches have been proposed in the literature to achieve selfreconfiguration, however, as the complexity of the adaptive system grows, designing and managing the set of reconfiguration rules becomes difficult and error-prone. To tackle this limitation, we propose a new approach that uses a search-based evolutionary algorithm that explores valid configurations to find the most relevant one given a specific running context. Another salient advantage of our approach is the re-exploitation, in the context of adaptability, of the design knowledge and existing model-based technologies through the reuse of the feature model of the system.

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