PhD Dissertation Report: From Benchmarking Optimization Heuristics to Dynamic Algorithm Configuration

Benchmarking plays an important role in understanding the strengths and weaknesses of optimization heuristics. However, data collected from one benchmarking study is not limited in its use to answering that single original research question. By sharing and re-using data, we can gain new insights into, for example, the complementarity of different algorithms. In this thesis, we start from the creation of a software framework which supports the benchmarking process, and utilize the resulting data to investigate meta-algorithmic questions, starting from algorithm configuration and moving to dynamic algorithm selection: switching between optimizers during the search process.