A genetic algorithm for self-optimization in safety-critical resource-flow systems

Organic Computing tries to tackle the rising complexity of systems by developing mechanisms and techniques that allow a system to self-organize and possess life-like behavior. The introduction of self-x properties also brings uncertainty and makes the systems unpredictable. Therefore, these systems are hardly used in safety-critical domains and their acceptance is low. If those systems should also profit from the benefits of self-x properties, behavioral guarantees must be provided. In this paper, a genetic algorithm for the self-optimization of resource-flow systems is presented. Further, its integration into an architecture which allows to provide behavioral guarantees is shown.