Sven Tomforde Knihy

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.

This book presents the results of the OC-DDC 2018. Successful participants have been invited to extend their abstracts submitted to the event towards a full book chapter by taking reviews and feedback received at the event in Wurzburg into account. The participants prepared an initial extended abstract, helped to perform a sophisticated review process, and finally came up with interesting articles summarising their current work in the context of Organic Computing. Hence, the book also gives an overview of corresponding research activities in the field in Germany for the year 2018. The collection of contributions reflects the diversity of the different aspects of Organic Computing. In the following, we outline the contributions contained in this book.

This book presents the results of the OC-DDC 2017. Successful participants have been invited to extend their abstracts submitted to the event towards a full book chapter by taking reviews and feedback received at the event in Bochum into account. Seven of the participants prepared a contribution to this book, helped to perform a sophisticated review process, and finally came up with interesting articles summarising their current work in the context of Organic Computing. Hence, the book also gives an overview of corresponding research activities in the field in Germany for the year 2017. The collection of contributions reflects the diversity of the different aspects of Organic Computing. Furthermore, group discussions during the OC-DDC resulted in a contribution that aggregates the ideas of the participants related to applied machine learning for Organic Computing systems.

This book presents the results of the Organic Computing - Doctoral Dissertation Colloqium 2016. Successful participants have been invited to extend their abstracts submitted to the event towards a full book chapter by taking reviews and feedback received at the event in Duisburg into account. Hence, the book also gives an overview of corresponding research activities in the field in Germany for the year 2016. The collection of contributions reflects the diversity of VIII Preface the different aspects of Organic Computing. Furthermore, group discussions during the OC-DDC resulted in a contribution that aggregates the ideas of the participants related to applied machine learning for Organic Computing systems. This is complemented by an article about the term “Organic Computing”. In the following, we outline the contributions contained in this book.

This book consists of fourteen different contributions that can be grouped into five major categories reflecting the different aspects of current OC research in general: (1) trustworthiness, (2) swarm behaviour, (3) security and testing, (4) self-learning, and (5) hardware aspects.

This book consists of twelve different contributions that reflect several aspects of OC research. Therefore, we introduced four major categories summarizing the contents of the contributions as well as describing the different aspects of OC research in general: (1) design and architectures, (2) trustworthiness, (3) self-learning, and (4) self-x properties.