My primary research focus revolves around the automated design of metaheuristic algorithms (e.g., using hyper-heuristics). Metaheuristics are nature-inspired stochastic optimization methods that have proven to be highly effective in solving real-world problems where many exact methods are not applicable or fail to produce comparable results. Practitioners often encounter scenarios where they need to solve similar instances of optimization problems repeatedly, and metaheuristics specifically adapted to such instances have great potential over general search methods.
Automated metaheuristic design is an alternative to the complex and labor-intensive task of tailoring these algorithms by hand, and not only makes custom algorithms accessible to a much wider range of users, but also potentially outperforms human-designed algorithms by thoroughly exploring different design alternatives.
As part of my research, I am also interested in the following topics:
- parameter control and tuning methodologies for metaheuristics
- coevolutionary approaches
- parallel and distributed algorithms
- combining metaheuristics with machine learning
- modular metaheuristic frameworks (e.g., using Rust)
Wissenschaftlicher Mitarbeiter am Lehrstuhl Organic Computing der Universität Augsburg
|Wissenschaftliche Hilfskraft am Lehrstuhl Organic Computing der Universität Augsburg
|Master-Studium im Fach Informatik an der Universität Augsburg
|Bachelor-Studium im Fach Informatik an der Universität Augsburg
|Studentische Arbeiten am Lehrstuhl Organic Computing
|Seminar Organic Computing (Bachelor)
|Seminar Organic Computing (Master)