Simon Wessing

Simon Wessing, Mike Preuss

Efficient global optimization is a popular algorithm for the optimization of expensive multimodal black-box functions. One important reason for its popularity is its theoretical foundation of global convergence. However, as the budgets in expensive optimization are very small, the asymptotic properties only play a minor role and the algorithm sometimes comes off badly in experimental comparisons. Many alternative variants have therefore been proposed over the years. In this work, we show experimentally that the algorithm instead has its strength in a setting where multiple optima are to be identified.

Simon Wessing

The ultimate goal of all optimization methods is to solve real-world problems. For a successful project execution, knowledge about optimization and the application has to be pooled. As it is too inefficient to highly train one person in both fields, a team of experts is usually put together. Unfortunately, communication errors must be expected when several people collaborate. In this work, we deal with the avoidance and the repair of these communication errors. The tools proposed in this regard are, among others, the algorithm engineering cycle, checklists for structuring communication, and knowledge databases. The discussion is enriched with examples from continuous optimization, but most tools have a much wider applicability, even beyond optimization.