Today, we proudly celebrate Lars van der Water’s successful defense of his Master’s thesis, Exploring Vast Design Spaces with Hyperheuristics: Theoretical Foundations and Autotuning Implementation, at the University of Amsterdam. This work has been conducted in connection with the DSE2.0 project, a research collaboration between University of Amsterdam, Leiden University, and ASML, co-funded by NWO and TNO-ESI as a part of the Mastering Complexity (MasCot) Program.
Lars’ thesis addresses the growing complexity in designing Distributed Cyber-Physical Systems, which are increasingly vital to infrastructure and industry. Traditional Design Space Exploration methods struggle with scalability, algorithm selection, and parameter tuning, creating a bottleneck in efficient exploration of system designs. To overcome this, this work explores hyperheuristics (HHs) as a higher-level domain-agnostic approach to automate the selection and tuning of metaheuristics. Key contributions include a modular framework for integrating HH strategies, and empirical insights into the trade-offs between performance, effort, and computational cost in autotuning. Experiments show promising results for auto-tuning of simpler meta-heuristic search algorithms like Gravitational Search and Particle Swarm Optimization, but revealing limitations with more complex ones like Genetic Algorithms.
We sincerely thank Lars for the excellent collaboration and wish him all the best in the next chapter of his career!
















