Transfer Learning for Automated Test Case Prioritization Using XCSF

Rosenbauer, Lukas; Pätzel, David; Stein, Anthony; Hähner, Jörg

doi:10.1007/978-3-030-72699-7_43

Cited by 12 publications

(2 citation statements)

References 21 publications

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“…Rosenbauer et al [40] investigate the effect of a simple population transformation that enables transfer learning for XCSF. The transformation essentially only resets rule fitness, the expected rule error estimates as well as some of the bookkeeping parameters while assuming that the dimensionality of the input space stays the same.…”

Section: Real World Applicationsmentioning

confidence: 99%

An overview of LCS research from 2021 to 2022

Heider

Pätzel

Wagner

2022

Proceedings of the Genetic and Evolutionary Computation Conference Companion

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The International Workshop on Learning Classifier Systems (IWLCS) is an annual workshop at the GECCO conference where new concepts and results regarding learning classifier systems (LCSs) are presented and discussed. One recurring part of the workshop agenda is a presentation that reviews and summarizes the advances made in the field over the last year; this is intended to provide an easy entry point to the most recent progress and achievements. The 2020 and 2021 presentations were accompanied by survey workshop papers, a practice which we hereby continue. We give an overview of all the LCS-related publications from 11 March 2021 to 10 March 2022. The 42 publications we review are grouped into six overall topics: Formal theoretic advances, new LCS architectures, LCS-based reinforcement learning, algorithmic improvements to existing LCSs, combinations of LCS and Deep Learning models and, finally, a variety of applications of LCSs. CCS CONCEPTS• Computing methodologies → Rule learning; Genetic algorithms; • General and reference → Surveys and overviews.

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Section: Real World Applicationsmentioning

confidence: 99%

An overview of LCS research from 2021 to 2022

Heider

Pätzel

Wagner

2022

Proceedings of the Genetic and Evolutionary Computation Conference Companion

Self Cite

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“…For instance, they have been applied in typical CPS use cases such as self-adaptive traffic management [Ste+17a;STH16], autonomous parameter adjustment of data communication protocols [TH11], or Industry 4.0 [HPH20]. Beyond the domain of CPS, Rosenbauer et al [Ros+21] applied XCS for function approximation (XCFS) for automated test case prioritization and Stein et al targeted a smart cameras application [Ste+17b].…”

Section: Learning -Lcs and Adaptation Languagesmentioning

confidence: 99%

Comprehensive adaptive middleware for mixed-critical cyber-physical networks

Feist

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Cyber Physical Systems (CPS) are growing more and more complex due to the availability of cheap hardware, sensors, actuators and communication links. A network of cooperating CPSs (CPN) additionally increases the complexity. This poses challenges as well as it offers chances: the increasing complexity makes it harder to design, operate, optimize and maintain such CPNs. However, on the other side an appropriate use of the increasing resources in computational nodes, sensors, actuators can significantly improve the system performance, reliability and flexibility. Therefore, self-X features like self-organization, self-adaptation and self-healing are key principles for such systems. Additionally, CPNs are often deployed in dynamic, unpredictable environments and safety-critical domains, such as transportation, energy, and healthcare. In such domains, usually applications of different criticality level exist. In an automotive environment for example, the brake has a higher criticality level regarding safety as the infotainment. As a result of mixed-criticality, applications requiring hard real-time guarantees compete with those requiring soft real-time guarantees and best-effort application for the given resources within the overall system. This leads to the need to accommodate multiple levels of criticality while ensuring safety and reliability, which increases the already high complexity even more. This thesis deals with the question on how to conveniently, effectively and efficiently handle the management and complexity of mixed-critical CPNs (MC-CPNs). Since this cannot be done by the system developer without the assistance of the system itself any longer, it is essential to develop new approaches and techniques to ensure that such systems can operate under a range of conditions while meeting stringent requirements. Based on five research hypothesis, this thesis introduces a comprehensive adaptive mixed-criticality supporting middleware for Cyber-Physical Networks (Chameleon), which efficiently and autonomously takes care of the management and complexity of CPNs with regard to the mixed-criticality aspect. Chameleon contributes to the state-of-art by introducing and combining the following concepts: - A comprehensive self-adaption mechanism on all levels of the system model is provided. - This mechanism allows a flexible combination of parametric and structural adaptation actions (relocation, scheduling, tuning, ...) to modify the behavior of the system. - Real-time constraints of mixed-critical applications (hard real-time, soft real-time, best-effort) are considered in all possible adaptation conditions and actions by the use of the importance parameter. - CPNs are supported by the introduction of different scopes (local, system, global) for the adaptation conditions and actions. This also enables the combination of different scopes for conditions and actions. - The realization of the adaptation with a MAPE-K loop instantiated by a distributed LCS allows for real-time capable reasoning of adaptation actions which also works on resource-spare systems. - The developed rule language Rango offers an intuitive way to specify an initial rule set for LCS in the context of CPS/CPNs and supports the system administrators in the process of rule set generation.

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