Prof.dr. Peter A.N. Bosman
Senior Researcher at Centrum Wiskunde & Informatica (CWI)
(Center for Mathematics and Computer Science)
Professor of Evolutionary Algorithms at Delft University of Technology
+31(0)20 592 4265
Prof.dr. Peter A.N. Bosman is a senior researcher heading the Medical Informatics (MI) subgroup of the Life Sciences and Health (LSH) research group at the Centrum Wiskunde & Informatica (CWI) (Center for Mathematics and Computer Science) located in Amsterdam, the Netherlands. He further has a part-time professor (in Dutch: deeltijdhoogleraar) position at Delft University of Technology in the Algorithmics group of the Department of Software Technology in the Faculty of Electrical Engineering, Mathematics, and Computer Science. Prof.dr. Bosman was formerly affiliated with the Intelligent Systems research group of CWI and before that with Utrecht University, where he also obtained his M.Sc. and Ph.D. degrees in Computer Science.
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.
Prof.dr. Bosman's applied research focus is on the use of (model-based) EAs to solve key problems in the Life-Science and Health (LSH) domain that require optimization and/or machine learning. A specific focus is on improving mathematics and computer science related aspects in radiation oncology, such as automated treatment planning, deformable image registration and 3D dose reconstruction. Previous application areas have included (smart) energy and logistics, dynamic pricing of goods for revenue management, optimization of patient flows in hospitals and dynamic routing of vehicles for transportation purposes.
Prof.dr. Bosman has (co-)authored over 100 refereed publications, out of which 4 received best paper awards. According to Google Scholar, his h-index is 30 with a total of 3197 citations to his works (as measured on August 11, 2019). He is an officer and business committee member of SIGEVO, the ACM special interest group on Genetic and Evolutionary Computation, as well as program committee member of various major conferences and journals in the EA field and related fields. In 2017, Prof.dr. Bosman was the General Chair of the main conference in the field of EAs: the Genetic and Evolutionary Computation Conference (GECCO). He has furthermore organized various workshops and tutorials on various EA related topics and has been (co-)track chair and (co-)local chair at GECCO.
Finally, the (co-)acquired research grant funding by Prof.dr. Bosman totals over €5M, which includes one KWF project, one STW-KWF partnership project, four NWO projects, one KiKa project, and one EIT ICT Labs project that together fund(ed) various positions (5×postdoc, 15×Ph.D. student, 1×M.D.-Ph.D. student, 2×radiation therapy technologist, 2×scientific programmer), and various high-performance computing hardware.