My research concerns the design and application of model-based Evolutionary Algorithms (EAs) to solve optimization problems that are typically complex and contain uncertainties (e.g. complex simulations). These problems often require taking a black-box optimization (BBO) perspective, i.e. (virtually) nothing is known about the optimization problem at hand. Often, the EAs I study are so-called Estimation-of-Distribution Algorithms (EDAs), in which probabilistic models are built and sampled and used to guide the search for better solutions so as to get the most out of previously performed evaluations. I study the design of (model-based) EAs for both single- and multi-objective optimization problems as well as for optimization problems in the presence of uncertainties, i.e. problems with stochastic and/or dynamically (i.e. over time) changing objectives. Applications include the optimization of patient flows in hospitals, the dynamic routing of vehicles for transportation purposes, dynamic pricing of goods for revenue management and (network design for) energy systems.

On this website you can find more information on my research via a list of my scientific publications, some of the source code I developed in the course of my research and my resume. You can also find a little more background information on me. To navigate this website, you can use the main menu on your left and the sub menus that appear at the top of each page.