Runtime adaptation of technical systems

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Focusing on a novel system design approach, this book presents a framework that enables self-configuration and self-improvement for parametrisable systems during runtime, enhancing their adaptivity and robustness. It explores the integration of machine learning techniques, specifically two innovative variants of Learning Classifier Systems and Fuzzy Classifier Systems, to manage self-improvement tasks. Real-world applications in areas such as vehicular traffic, data communication, and function approximation are utilized to evaluate the framework's effectiveness.