Prof.dr. Bosman's fundamental research focus is on the design and application of evolutionary algorithms (EAs) for single- and multi-objective optimization, and machine learning. The optimization problems considered are typically complex to an extent where a black-box optimization (BBO), or at least a grey-box optimization (GBO), perspective is required, i.e. virtually no information (BBO) or limited information (GBO) is available (or properly understood) about the optimization problem at hand. The designed EAs are moreover mostly model-based, meaning that a specific model is used to capture and exploit problem-specific features to guide the search for high-quality solutions more effectively and efficiently and get the most out of previously performed evaluations. Such models may be derived by hand or, if this isn't possible (as in e.g. the BBO case), be learned online, i.e. during optimization, using techniques from fields such as machine learning and data mining. For problems where efficient (problem-specific) heuristic optimization techniques (i.e. local search (LS) techniques) are available or can be derived, model-based EAs are furthermore a very solid basis for hybridization to obtain the best of both worlds in terms of efficiency and effectiveness, resulting in state-of-the-art optimization algorithms for specific problems.