12 citations · 15 across the 5 of their papers we have counts for
5 papers
Performance and Explainability Requirements of Evolutionary Algorithms in Real-World Physics-Informed Optimization
Helena Stegherr, Michael Heider, Nils Meyer +7
Evolutionary computation offers a variety of tools to solve complex real-world optimization problems. However, research often focuses on smaller, simplified problems and optimizati…
Pareto-Optimal Anytime Algorithms via Bayesian Racing
Jonathan Wurth, Helena Stegherr, Neele Kemper +2
Selecting an optimization algorithm requires comparing candidates across problem instances, but the computational budget for deployment is often unknown at benchmarking time. Curre…
Investigating the Impact of Independent Rule Fitnesses in a Learning Classifier System
Michael Heider, Helena Stegherr, Jonathan Wurth +2
Achieving at least some level of explainability requires complex analyses for many machine learning systems, such as common black-box models. We recently proposed a new rule-based…
Learning Classifier Systems for Self-Explaining Socio-Technical-Systems
Michael Heider, Helena Stegherr, Richard Nordsieck +1
In socio-technical settings, operators are increasingly assisted by decision support systems. By employing these, important properties of socio-technical systems such as self-adapt…
Separating Rule Discovery and Global Solution Composition in a Learning Classifier System
Michael Heider, Helena Stegherr, Jonathan Wurth +2
While utilization of digital agents to support crucial decision making is increasing, trust in suggestions made by these agents is hard to achieve. However, it is essential to prof…