Publications (7)
A Performance Analysis of Lexicase-Based and Traditional Selection Methods in GP for Symbolic Regression
Alina Geiger, Dominik Sobania, Franz Rothlauf
In recent years, several new lexicase-based selection variants have emerged due to the success of standard lexicase selection in various application domains. For symbolic regressio…
Down-Sampled Epsilon-Lexicase Selection for Real-World Symbolic Regression Problems
Alina Geiger, Dominik Sobania, Franz Rothlauf
Epsilon-lexicase selection is a parent selection method in genetic programming that has been successfully applied to symbolic regression problems. Recently, the combination of rand…
SkillMOO: Multi-Objective Optimization of Agent Skills for Software Engineering
Jingzhi Gong, Ruizhen Gu, Zhiwei Fei +7
Agent skills are increasingly used to configure coding agents for software engineering (SE) tasks, yet current practice treats them as static, hand-crafted assets, or evolved on pa…
Was Tournament Selection All We Ever Needed? A Critical Reflection on Lexicase Selection
Alina Geiger, Martin Briesch, Dominik Sobania +1
The success of lexicase selection has led to various extensions, including its combination with down-sampling, which further increased performance. However, recent work found that…
ROIDS: Robust Outlier-Aware Informed Down-Sampling
Alina Geiger, Martin Briesch, Dominik Sobania +1
Informed down-sampling (IDS) is known to improve performance in symbolic regression when combined with various selection strategies, especially tournament selection. However, recen…
LLM-Guided Genetic Improvement: Envisioning Semantic Aware Automated Software Evolution
Karine Even-Mendoza, Alexander Brownlee, Alina Geiger +4
Genetic Improvement (GI) of software automatically creates alternative software versions that are improved according to certain properties of interests (e.g., running-time). Search…