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Item type:Publication, Access status: with files , Can explainability metrics improve genetic programming? Lessons from 2048(Association for Computing Machinery (ACM), 2026-08-13); ; ; Genetic programming (GP) produces solutions that, while often less performant than neural networks, are uniquely amenable to analysis. In domains like game-playing, this explainability could reveal why certain policies succeed or fail-but such insights are rarely leveraged to improve the algorithms themselves. Here, we analyze a GP algorithm for 2048, testing whether structural, behavioral, or semantic metrics of evolved policies correlate with performance. While structural metrics showed no predictive power and behavioral features yielded ambiguous results, we identified a semantic feature that correlated with policy quality. Using this insight, we designed a mutation operator that improved performance. Though modest, this improvement suggests that explainability metrics can guide operator design, even when broader explanatory goals remain unmet. More broadly, our work highlights a potential advantage of GP: its solutions may be analyzable in ways that opaque methods like neural networks are not.Publicationtype: Conference Paper2 3 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Ride-sharing simulation for optimisation(Association for Computing Machinery (ACM), 2026-08-13); ; Optimisation research requires possibilities to test and evaluate new algorithms and algorithm adaptions. Two interesting problem classes-relevant to society-that covers shortest path problems and resource allocation in a very dynamic environment, with the possibility of running endlessly, are ride-hailing and ride-sharing. This paper presents an extensible simulation framework for ride-hailing and ride-sharing problems that allows researchers to create solutions in any program language, provides a graphical user-interface, and automatic evaluation.Publicationtype: Conference Paper2 3 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Enhancing surrogate model usability for optimisation experts through extended ML support in EvoAl(Association for Computing Machinery (ACM), 2026-08-13); ; ; Surrogate models are a crucial aspect in many real-world application optimisation problems. Thus, optimisation experts do not only have to focus on the optimisation problem at hand, but also work on selecting and configuring appropriate models. This may be challenging, when no extensive expertise in machine learning (ML) is present. To address this issue, we extend the open-source data science research tool EvoAl to better support optimisation practitioners in focusing on their core tasks. Specifically, we enhance EvoAl’s machine learning language to accommodate the growing complexity of surrogate modelling workflows. Our contributions include expanding the range of available models, introducing runtime validation of model configurations, and enabling the integration of pre-trained models via ONNX. These improvements lower the barrier to applying advanced ML techniques in optimisation, thereby facilitating more efficient and robust surrogate-based optimisation in practical applications.Publicationtype: Conference Paper5 3 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , 3. Übungsblatt : Informatik für Ingenieur*Innen - WiSe 2025/26(2025-10-31); ; This is a worksheet used in the lecture "Informatik für Ingenieur*innen" in the winter semester 2025/26. Task 2 was modified as part of a research based intervention.Publicationtype: Other25 8 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Automatic security-flaw detection - towards a fair evaluation and comparisonThreat Modeling is an essential step in secure software system development. It is a (so far) manual, attacker-centric approach for identifying architecture-level security flaws during the planning phase of software systems. In recent years, academia has presented ideas to automate threat detection that do not focus on a particular class of security flaws but offer means of pattern-based security flaw descriptions. However, comparing presented ideas (tools) for automated threat detection contains the potential for unwilling bias or restricted information content. In this work, we investigate the process of comparing automatic security flaw detection tools, clarify common pitfalls during this process, and propose a fair, reproducible, and informative comparison approach to be used as a community standard. We additionally discuss the necessary steps for the community to effectively implement this approach and support improved comparisons and evaluations in the future. We use a previously published case study to determine problems with current comparison techniques and classify different levels of comparison to be used for future reference as our main contribution. As a consequence, we propose using a model-based approach for specifying security flaws and apply an existing natural language-based catalogue to this model-based approach. Furthermore, we introduce an inspection process model (for providing a standard to specify findings of a threat detection process) to streamline the evaluation and comparisons of automatic security flaw detection tools. We provide an exemplary evaluation of this detection guideline and inspection process model along the lines of both automatic approaches from the original case study. All artefacts of the work are publicly available to support the research community and to create a common baseline for future tool comparisons.Publicationtype: Journal Article11 8