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Prof.dr. Peter A.N. Bosman
Group leader at Centrum Wiskunde & Informatica (CWI)
(Dutch National Research Institute for Mathematics and Computer Science)
Evolutionary Intelligence group
Professor of Evolutionary Algorithms at Delft University of Technology
Algorithmics group
Department of Software Technology
Faculty of Electrical Engineering, Mathematics and Computer Science
Peter.Bosman@cwi.nl
http://homepages.cwi.nl/˜bosman
+31(0)20 592 4265
Overview
Prof.dr. Peter A.N. Bosman is the group leader of the Evolutionary Intelligence research group at the
Centrum Wiskunde & Informatica (CWI) (Center for Mathematics and Computer Science) in Amsterdam,
the Netherlands. He further is a part-time full professor (in Dutch: deeltijdhoogleraar) 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 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 modern model-based
Evolutionary Algorithms (EAs) for single- and multi-objective optimization, and Machine Learning (ML).
On the ML side, of particular interest is the use of model-based EAs to achieve eXplainable AI (XAI). On
the optimization side, 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 problem
at hand. In model-based EAs, a model is used to capture and exploit problem-specific features to guide
the search for high-quality solutions more effectively and efficiently. Such models may be derived by
hand or, if this is not possible (as in the BBO case), be learned during optimization, using e.g., ML
techniques. Of particular interest is the Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA)
family, which was co-founded by prof.dr. Bosman. Today, this family includes GPU-accelerated versions
for (multi-objective) optimization with real-world applications in the medical domain and a version for
genetic programming (GP-GOMEA), which, when used for symbolic regression, has been proven to be
among the state-of-the-art, offering a large potential for XAI purposes.
Prof.dr. Bosman’s applied research focus is on the use of (model-based) EAs to solve real-world
problems that require optimization and/or machine learning, which are often multi-objective in nature,
together with industry- and societal partners. A key application area is the medical domain, where
research outcomes have successfully been translated into clinical practice (in particular the automated
optimization of brachytherapy treatment plans for prostate cancer, at Amsterdam UMC). Additional
application areas include (smart) energy systems, revenue management, and logistics.
Prof.dr. Bosman has (co-)authored over 250 peer-reviewed publications, out of which 9 received best
paper awards and 11 more were nominated for a best paper award. Various other awards include 2
silver Humies awards (in 2021 and in 2019) for obtaining real-world human-competitive awards with
EAs (in the medical domain). He is currently chair of SIGEVO, the ACM special interest group on Genetic
and Evolutionary Computation and was formerly an officer, executive board, and business committee
member of SIGEVO. He is furthermore program committee member of all major conferences and various
journals in the EA field and related fields. Prof.dr. Bosman has been general chair, track chair, and local
chair as well as organizer of various workshops and tutorials at the main conference in the field of EAs -
the Genetic and Evolutionary Computation Conference (GECCO). According to Google Scholar, in
September 2024, his h-index was 42 with over 6000 citations to his works.
Finally, the (co-)acquired research grant funding by Prof.dr. Bosman totals over €12M, which includes
funding from the Dutch research council, the Dutch cancer society, the Dutch children cancer-free
foundation, and the European Innovation Council. Together, these grants support(ed) various scientific
research positions (including 28 Ph.D. students and 7 postdocs, as well as various radiation therapy
technologists, and scientific programmers), and various high-performance computing hardware.
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Personalia
Name
Peter A.N. Bosman
Date of birth
December 25, 1975
Location of birth
Utrecht, The Netherlands
Nationality
Dutch
Professional positions held
2022-current
Group leader (Evolutionary Intelligence research group) at the Center for Mathematics and Computer
Science (in Dutch: Centrum Wiskunde & Informatica (CWI)) in Amsterdam, the Netherlands
2018-current
Professor of Evolutionary Algorithms at Delft University of Technology in Delft, the Netherlands (part-
time professorship; in Dutch: “deeltijdhoogleraar”)
2010-2022
Senior researcher (tenured) at the Center for Mathematics and Computer Science (in Dutch: Centrum
Wiskunde & Informatica (CWI)) in Amsterdam, the Netherlands
2004-2010
Postdoctoral researcher (fixed term) at the Center for Mathematics and Computer Science (in Dutch:
Centrum Wiskunde & Informatica (CWI)) in Amsterdam, the Netherlands
2002-2004
Assistant professor (fixed term) at the Institute for Information and Computing Sciences at Utrecht
University in Utrecht, the Netherlands
Professional education
2018
Professional course on management in research organizations (in Dutch: Management in
OnderzoeksOrganisaties (MIOO)) at Leeuwendaal, the Netherlands (https://www.leeuwendaal.nl/)
2004
Basic university teaching qualification (in Dutch: BasisKwalificatie Onderwijs (BKO)) at Utrecht
University, the Netherlands (diploma date: 20-12-2004)
1998-2002
Ph.D. student (in Dutch: Assistent in Opleiding (AiO)) at the Institute for Information and Computing
Sciences at Utrecht University in Utrecht, the Netherlands (graduation date: 20-05-2003)
1994-1998
Masters (in Dutch: Doctoraal) computer science at Utrecht University in Utrecht, the Netherlands (cum
laude, exam date: 25-08-1998)
1988-1994
Atheneum at the Spectrum College in Utrecht, the Netherlands
Awards and nominations
Best paper awards
• T.M. Deist, M. Grewal, F.J.W.M. Dankers, T. Alderliesten, and P.A.N. Bosman. Multi-Objective Learning
using HV Maximization. In M. Emmerich et al., editors, Proceedings of the International Conference on
Evolutionary Multi-Criterion Optimization - EMO 2023, pages 103-117, Springer-Verlag, Berlin, 2023.
• A. Chebykin, T. Alderliesten, and P.A.N. Bosman. Evolutionary Neural Cascade Search across
Supernetworks. In J. Fieldsend et al., editors, Proceedings of the Genetic and Evolutionary Computation
Conference - GECCO-2022, pages 1038–1047, ACM Press, New York, New York, 2022. (NE track)
• D. Liu, M. Virgolin, T. Alderliesten, and P.A.N. Bosman. Evolvability degeneration in multi-objective
genetic programming for symbolic regression. In J. Fieldsend et al., editors, Proceedings of the Genetic
and Evolutionary Computation Conference - GECCO-2022, pages 973–981, ACM Press, New York, New
York, 2022. (GP track)
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• A. Dushatskiy, T. Alderliesten, and P.A.N. Bosman. A Novel Surrogate-assisted Evolutionary Algorithm
Applied to Partition-based Ensemble Learning. In F. Chicano et al., editors, Proceedings of the Genetic
and Evolutionary Computation Conference - GECCO-2021, pages 583–591, ACM Press, New York, New
York, 2021. (GA track)
• T. den Ottelander, A. Dushatskiy, M. Virgolin, and P.A.N. Bosman. Local Search is a Remarkably Strong
Baseline for Neural Architecture Search. In H. Ishibuchi et al., editors, Proceedings of the International
Conference on Evolutionary Multi-Criterion Optimization - EMO 2021, pages 465-479, Springer-Verlag,
Berlin, 2021.
• N.H. Luong, H. La Poutré, and P.A.N. Bosman. Exploiting Linkage Information and Problem-Specific
Knowledge in Evolutionary Distribution Network Expansion Planning. In S. Silva et al., Proceedings of
the Genetic and Evolutionary Computation Conference - GECCO-2015, pages 1231-1238, ACM Press,
New York, New York, 2015. (RWA track)
• T. Brys, M. Drugan, P.A.N. Bosman, M. De Cock, and A. Nowé. Solving Satisfiability in Fuzzy Logics by
Mixing CMA-ES. In C. Blum et al, editors, Proceedings of the Genetic and Evolutionary Computation
Conference - GECCO-2013, pages 1125-1132, ACM Press, New York, New York, 2013. (IGEC track)
• P.A.N. Bosman. The Anticipated Mean Shift and Cluster Registration in Mixture-based EDAs for Multi-
Objective Optimization. In J. Branke et al., editors, Proceedings of the Genetic and Evolutionary
Computation Conference - GECCO-2010, pages 351-358, ACM Press, New York, New York, 2010. (EDA track)
• P.A.N. Bosman. On Empirical Memory Design, Faster Selection of Bayesian Factorizations and
Parameter-Free Gaussian EDAs. In G. Raidl et al., editors, Proceedings of the Genetic and Evolutionary
Computation Conference - GECCO-2009, pages 389-396, ACM Press, New York, New York, 2009.
(Award won at the Belgium-Netherlands Artificial Intelligence Conference - BNAIC-2009: best
internationally published paper award, referred to at the BNAIC conference as B-type papers)
Best paper nominations (additional to the papers for which an award was won)
• J. Koch, T. Alderliesten, and P.A.N. Bosman. Simultaneous Model-Based Evolution of Constants and
Expression Structure in GP-GOMEA for Symbolic Regression. In Parallel Problem Solving from Nature
- PPSN XVIII. pages 238-255, Springer-Verlag, Berlin, 2024.
• L.R.M. Dickhoff, E.M. Kerkhof, H.H. Deuzeman, C.L. Creutzberg, T. Alderliesten, and P.A.N. Bosman.
Adaptive objective configuration in bi-objective evolutionary optimization for cervical cancer
brachytherapy treatment planning. In J. Fieldsend et al., editors, Proceedings of the Genetic and
Evolutionary Computation Conference - GECCO-2022, pages 1173–1181, ACM Press, New York, New
York, 2022. (RWA track)
• A. Bouter, S.C. Maree, T. Alderliesten, and P.A.N. Bosman. Leveraging Conditional Linkage Models in
Gray-box Optimization with the Real-Valued Gene-pool Optimal Mixing Evolutionary Algorithm. In
J.A. Lozano et al., editors, Proceedings of the Genetic and Evolutionary Computation Conference -
GECCO-2020, pages 603-611, ACM Press, New York, New York, 2020. (ENUM track)
• K. Orphanou, D. Thierens, and P.A.N. Bosman. Learning Bayesian Network Structures with GOMEA In
H. Aguirre et al., editors, Proceedings of the Genetic and Evolutionary Computation Conference -
GECCO-2018, pages 1007-1014, ACM Press, New York, New York, 2018. (GA track)
• Bouter, T. Alderliesten, C. Witteveen, and P.A.N. Bosman. Exploiting linkage information in real-
valued optimization with the real-valued gene-pool optimal mixing evolutionary algorithm. In G.
Ochoa et al., editors, Proceedings of the Genetic and Evolutionary Computation Conference - GECCO-
2017, pages 705-712, ACM Press, New York, New York, 2017. (ENUM track)
• P.A.N. Bosman, N.H. Luong, and D. Thierens. Expanding from Discrete Cartesian to Permutation Gene-pool
Optimal Mixing Evolutionary Algorithms. In Proceedings of the Genetic and Evolutionary Computation
Conference - GECCO-2016, pages 637-644, ACM Press, New York, New York, 2016. (GA track)
• K.L. Sadowski, D. Thierens and P.A.N. Bosman, Combining Model-Based EAs for Mixed-Integer
Problems. In T. Bartz-Beielstein et al., editors, In Parallel Problem Solving from Nature - PPSN XII. pages
342-351, Springer-Verlag, Berlin, 2014.
• T. Brys, M. Drugan, P.A.N. Bosman, M. De Cock, and A. Nowé. Local Search and Restart Strategies for
Satisfiability Solving in Fuzzy Logics. In Proceedings of the IEEE Symposium Series on Computational
Intelligence - SSCI-2013, IEEE Press, Piscataway, New Jersey, 2013.
• P.A.N. Bosman and D. Thierens. On Measures to Build Linkage Trees in LTGA. In C.A. Coello Coello et al.,
editors, Parallel Problem Solving from Nature - PPSN XII, pages 276-285, Springer-Verlag, Berlin, 2012.
• P.A.N. Bosman and H. La Poutré. Learning and Anticipation in Online Dynamic Optimization with
Evolutionary Algorithms: The Stochastic Case. In D. Thierens et al., editors, Proceedings of the Genetic
and Evolutionary Computation Conference - GECCO-2007, pages 1165-1172, ACM Press, New York,
New York, 2007. (GA track)
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• P.A.N. Bosman and E.D. de Jong. Combining Gradient Techniques for Numerical Multi-Objective
Evolutionary Optimization. In M. Keijzer et al., editors, Proceedings of the Genetic and Evolutionary
Computation Conference - GECCO-2006, pages 627-634, ACM Press, New York, New York, 2006. (EMO Track)
Competition-based awards
• Humies Silver Award (3000 USD) for Human-Competitive Results Produced by Genetic and
Evolutionary Computation, presented at the Genetic and Evolutionary Computation Conference
(GECCO) for the work presented in M. Virgolin, Z. Wang, B.V. Balgobind, I.W.E.M. van Dijk, J. Wiersma,
P.S. Kroon, G.O. Janssens, M. van Herk, D.C. Hodgson, L. Zadravec Zaletel, C.R.N. Rasch, A. Bel, P.A.N.
Bosman, and T. Alderliesten; Surrogate-free machine learning-based organ dose reconstruction for
pediatric abdominal radiotherapy. In Physics in Medicine & Biology; IOP Publishing; Bristol; 65(24),
p.245021, 2020.
• Humies Silver Award (3000 USD) for Human-Competitive Results Produced by Genetic and
Evolutionary Computation, presented at the Genetic and Evolutionary Computation Conference
(GECCO) for the work presented in S.C. Maree, N.H. Luong, E.S. Kooreman, N. van Wieringen, A. Bel,
K.A. Hinnen, H. Westerveld, B.R. Pieters, P.A.N. Bosman, and T. Alderliesten. Evaluation of bi-objective
treatment planning for high-dose-rate prostate brachytherapy – A retrospective observer study. In
Brachytherapy. 18(3), pages 396-403, 2019.
• Winner of the 2019 Competition on Niching Methods for Multimodal Optimization at the Genetic
and Evolutionary Computation Conference (GECCO): S.C. Maree, T. Alderliesten, and P.A.N. Bosman.
Benchmarking HillVallEA for the GECCO 2019 Competition on Multimodal Optimization. In arXiv
preprint. arXiv:1907.10988, 2019.
• Winner of the 2018 Competition on Niching Methods for Multimodal Optimization at the Genetic and
Evolutionary Computation Conference (GECCO): S.C. Maree, T. Alderliesten, D. Thierens, and P.A.N.
Bosman. Benchmarking the Hill-Valley Evolutionary Algorithm for the GECCO 2018 Competition on
Niching Methods Multimodal Optimization. In arXiv preprint. arXiv:1807.00188, 2018.
Miscellaneous awards (including for supervised PhD students and postdocs)
• ACM SIGEVO Best Dissertation award 2024, awarded at the Genetic and Evolutionary Computation
Conference (GECCO) for Ph.D. student Anton Bouter with this thesis Optimal Mixing Evolutionary Algorithms
for Large-Scale Real-Valued Optimization - Including Real-World Medical Applications.
• ACM SIGEVO Best Dissertation award 2021, awarded at the Genetic and Evolutionary Computation
Conference (GECCO) for Ph.D. student Marco Virgolin with this thesis Design and Application of Gene-pool
Optimal Mixing Evolutionary Algorithms for Genetic Programming.
• ESTRO – Elekta Brachytherapy Award 2019 at the European Society for Therapeutic Radiology and Oncology
(ESTRO) Conference: A. Bouter, T. Alderliesten, B.R. Pieters, A. Bel, Y. Niatsetski, and P.A.N. Bosman. Bi-
objective optimization of dosimetric indices for HDR prostate brachytherapy within 30 seconds. In
Proceedings of the European SocieTy for Radiotherapy & Oncology conference - ESTRO-2019. 2019.
• Cum Laude Poster Award sponsored by 12 Sigma at the SPIE Medical Imaging 2019 Conference: K.
Pirpinia, P.A.N. Bosman, J.-J. Sonke, M. van Herk, and T. Alderliesten. Evolutionary multi-objective
meta-optimization of deformation and tissue removal parameters improves the performance of
deformable image registration of pre- and post-surgery images. In Proceedings of the SPIE Medical
Imaging Conference 2019. 10949; doi:10.1117/12.2512760, SPIE, Bellingham, WA, 2019.
• Junior Brachytherapy Travel Grant Award at the European Society for Therapeutic Radiology and
Oncology (ESTRO) Conference (granted to PhD student S.C. Maree): S.C. Maree, E.S. Kooreman, N.H.
Luong, N. van Wieringen, A. Bel, E.C.M. Rodenburg, K.A. Hinnen, G.H. Westerveld, B.R. Pieters, P.A.N.
Bosman, and T. Alderliesten. Better plans and easy plan selection via bi-objective optimization for
HDR prostate brachytherapy. In Proceedings of the European SocieTy for Radiotherapy & Oncology
conference - ESTRO-2018. 2018.
• Junior Brachytherapy Travel Grant Award at the European Society for Therapeutic Radiology and
Oncology (ESTRO) Conference (granted to PhD student M.C. van der Meer): M.C. van der Meer, P.A.N.
Bosman, B.R. Pieters, Y. Niatsetski, T. Alderliesten, and A. Bel. Sensitivity of dose-volume indices to
organ reconstruction settings in HDR prostate brachytherapy. In Proceedings of the European SocieTy
for Radiotherapy & Oncology conference - ESTRO-2018. 2018.
• Best GEC-ESTRO Junior Presentation Award at the European Society for Therapeutic Radiology and
Oncology (ESTRO) Conference (granted to PhD student S.C. Maree): S.C. Maree, P.A.N. Bosman, Y.
Niatsetski, C. Koedooder, N. van Wieringen, A. Bel, B.R. Pieters, T. Alderliesten. Improved class
solutions for prostate brachytherapy planning via evolutionary machine learning. In Proceedings of
the European SocieTy for Radiotherapy & Oncology conference - ESTRO-2017, 2017.
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• Young Investigator Award at the International Conference on the use of Computers in Radiation
Therapy (ICCR) Conference (granted to PhD student K. Pirpinia): K. Pirpinia, P.A.N. Bosman, C.E. Loo,
A.N. Scholten, J.-J. Sonke, M. van Herk, and T. Alderliesten. Multi-objective optimization as a novel
weight-tuning strategy for deformable image registration applied to pre-operative partial-breast
radiotherapy. In U. Oelfke and M. Partridge, editors, Proceedings of the International Conference on
the use of Computers in Radiation Therapy - ICCR-2016, 2016.
Graduation awards
• P.A.N. Bosman. A General Framework and Development Environment for Interactive Visualizations
of Evolutionary Algorithms in Java and Using it to Investigate Recent Optimization Algorithms that
Use a Different Approach to Linkage Learning. Utrecht University M.Sc. thesis INF-SCR-98-15, 1998.
(The national computer-science graduation project runner-up award, awarded by "het Nederlands
Genootschap voor Informatica - NGI")
• P.A.N. Bosman. A General Framework and Development Environment for Interactive Visualizations
of Evolutionary Algorithms in Java and Using it to Investigate Recent Optimization Algorithms that
Use a Different Approach to Linkage Learning. Utrecht University M.Sc. thesis INF-SCR-98-15, 1998.
(The CIVI national graduation project award in the computer science and technical computer science
category, awarded by "de Koninklijke Hollandsche Maatschappij der Wetenschappen")
Grants
Research grants (total: € 12,631,328)
• P.A.N. Bosman and T. Alderliesten. New Strategies for the Early Diagnosis, Risk Stratification, and Co-
Management of Familial Hypercholesterolemia (workpackages therein – PI: F.J. Pinto). EIC Horizon-RIA
programme. Funds 2 postdocs. 2024. €859,000.
• P.A.N. Bosman and T. Alderliesten. Dynamic Integrative Risk forEcasting to Conquer overTreatment of
Ductal Carcinoma In Situ: DIRECT-DCIS (workpackages therein – PI: J. Wesseling). NWO NWA-ORC
Programme. Funds 6 PhD students, 1 postdoc and 1 scientific programmer. 2023. €2,789,093.
• T. Alderliesten and P.A.N. Bosman. Uitlegbare Kunstmatige Intelligentie. Stichting Gieskes-Strijbis Fonds.
Funds 3 Ph.D. students. 2021. €881,947.00.
• P.A.N. Bosman and T. Alderliesten. TRUST-AI – Transparent, Reliable and Unbiased Smart Tool for AI
(workpackages therein – PI: G. Figueira). EIC Horizon 2020 FET Proactive – Emerging Paradigms and
Communities programme. Funds 2 Ph.D. students and a senior researcher (4 years at 0.1 FTE) – within a
large European consortium. 2020. €698,795.
• P.A.N. Bosman and T. Alderliesten. DAEDALUS – Decentralized and Automated Evolutionary Deep
Architecture Learning with Unprecedented Scalability. NWO-TTW Open Technology Programme.
Funds 3 Ph.D. students and 1 scientific programmer (1 year). 2020. €1,086,368.
• P.A.N. Bosman and T. Alderliesten. EXAMINE – Evolutionary eXplainable Artificial Medical
INtelligence Engine. NWO Open Competition Domain Science – KLEIN-2 programme. Funds 2 Ph.D.
students. 2019. €554,076.
• T. Alderliesten, P.A.N. Bosman, L.J.A. Stalpers, B.R. Pieters. Fast, accurate, and insightful
brachytherapy treatment planning for cervical cancer through artificial intelligence. Dutch Cancer
Society (KWF, Project No. 12183). Funds 2 Ph.D. students, 1 MD-Ph.D. student, 1 scientific programmer
(2 years), and 1 radiation therapy technologist (5 years at 0.05 FTE). 2019. €928,404.
• P.A.N. Bosman and T. Alderliesten. FEDMix: Fusible Evolutionary Deep Neural Network Mixture
Learning from Distributed Data for Robust Medical Image Analysis. NWO-ENW Joint eScience and
Data Science across the Topsectors Programme. Funds 1 Ph.D. student. 2017. €249,563.
• T. Alderliesten and P.A.N. Bosman. Public-private partnership allowance for the Multi-Objective
Deformable Image Registration (MODIR) project. Ministry of Economic Affairs allowance program for
top consortia for knowledge and innovation (TKIs). 2018. €67,250.00.
• T. Alderliesten, P.A.N. Bosman, A. Bel. Multi-Objective Deformable Image Registration (MODIR) - An
Innovative Synergy of Multi-Objective Optimization, Machine Learning, and Biomechanical
Modeling for the Registration of Medical Images with Content Mismatch and Large Deformations.
NWO-TTW Open Technology Programme. Funds 1 postdoc (3 years at 1 FTE), 3 Ph.D. students, and 1
radiation therapy technologist (4 years at 0.5 FTE). 2017. €1,414,172.
• T. Alderliesten and P.A.N. Bosman. High Performance Computing System for Research into Mapping Out
more Accurately than Ever Before the Irradiation-Induced Long-Term Effects after Surviving Childhood
Cancer. Nijbakker-Morra Stichting. Funds 1 High Performance Computing System. 2017. €16,372.
• P.A.N. Bosman. Support for the GPU-based Acceleration of Gene-pool Optimal Mixing Evolutionary
Algorithms. NVIDIA GPU Grant Programme. Funds 1 Tesla X Pascal GPU card. 2016. €1,310.
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• P.A.N. Bosman, K. Hindriks, M. Neerincx, H. Merks, M. Grootenhuis and T. Alderliesten. Improving
Childhood Cancer Care when Parents Cannot be There - Reducing Medical Traumatic Stress in
Childhood Cancer Patients by Bonding with a Robot Companion. STW-KWF Partnership Programme.
Funds 1 postdoc (2 years at 1 FTE) and 2 PhD students. 2016. €757,560.
• T. Alderliesten and P.A.N. Bosman. High Performance Computing System for the Accurate
Reconstruction of the 3D Dose Distribution for Children with Cancer who have been Treated in the
Past. Maurits en Anna de Kock Stichting. Funds 1 High Performance Computing System. 2016. €16,279.
• P.A.N. Bosman, Y. Niatsetski, T. Alderliesten and A. Bel. ICT-based Innovations in the Battle against
Cancer - Next-Generation Patient-Tailored Brachytherapy Cancer Treatment Planning. NWO-EW
Innovatieve publiek-private samenwerking in ICT (IPPSI) - Technology Area (TA) programme. Funds 1
postdoc (3 years at 1 FTE) and 3 PhD students. 2015. €1,000,000.
• T. Alderliesten, A. Bel, C.R.N. Rasch, P.A.N. Bosman and C.M. Ronckers. 3D dose reconstruction for
children with long-term follow-up - Toward improved decision making in radiation treatment for
children with cancer. KiKa multiannual research projects. Funds 2 Ph.D. students and 1 postdoc (4
years at 0.15 FTE). 2014. €484,720.
• P.A.N. Bosman. Market-driven Simulation Software for Smart Energy Systems. EIT ICT Labs "Open
SES Experience Labs for Prosumers and New Services" activity in the innovation area "Smart Energy
Systems". Funds 1 scientific programmer (1 year at 0.2 FTE). 2013. €12,500.
• P.A.N. Bosman. Market-driven Simulation Software for Smart Energy Systems. EIT ICT Labs "Modular
Market Mechanisms Software for Smart Energy Systems" activity in the innovation area "Smart Energy
Systems". Funds 1 scientific programmer (1 year at 0.5 FTE). 2012. €32,500.
• D. Thierens, P.A.N. Bosman and H. La Poutré. Estimation of Distribution Algorithms for Mixed Continuous-
Discrete Problems. NWO free competition programme. Funds 1 Ph.D. student. 2011. €205,000.
• P.A.N. Bosman. Market and organisational mechanisms and intelligent planning methods for smart
energy systems. EIT ICT Labs "Modular Market Mechanisms Software for Smart Energy Systems"
activity in the innovation area "Smart Energy Systems". Funds 1 scientific programmer (1 year at 0.4
FTE). 2011. €28,000.
• H. La Poutré, P.A.N. Bosman and H. Slootweg. Computational Capacity Planning in Electricity
Networks. NWO Smart Energy Systems programme. Funds 2 Ph.D. students. 2010. €547,719.
Travel/visit grants
• P.A.N. Bosman. New Approaches to Handling Uncertainties in Optimization. Short Term Scientific
Mission (STSM) grant within the European Cooperation in Science and Technology (COST) scientific
programme on Combining Soft Computing Techniques and Statistical Methods to Improve Data Analysis
Solutions. Funds a research visit to J. Branke at Warwick Business School, Coventry, UK. 2010. €700.
Supervision
Postdocs
• Vacancy. 2026-2028.
• Vacancy. 2026-2028.
• Vacancy. 2026-2028.
• A. Dushatskiy. 2022-2023.
• S.C. Maree 2020-2020.
• M. Virgolin. 2019-2020.
• M. Camara. 2019-2019.
• T. Deist. 2019-2021.
• A.L. Rincon. 2017-2018.
• K. Orphanou. 2017-2018. (ERCIM Alain Bensoussan Fellowship programme)
• N.H. Luong. 2016-2019.
• I.W.E.M. van Dijk. 2015-2019.
Ph.D. students
• Vacancy. 2027-2030.
• Vacancy. 2027-2030.
• Vacancy. 2026-2029.
• Vacancy. 2026-2029.
• J. Koch. Delft University of Technology. Ph.D. defense expected 2027.
• C.J.A. Romme. University of Amsterdam. Ph.D. defense expected 2027.
• T. Schlender. Leiden University. Ph.D. defense expected 2026.
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• M.M. Oliveira. Delft University of Technology. Ph.D. defense expected 2026.
• D.M.F. Ha. Leiden University. Ph.D. defense expected 2026.
• R.J. Scholman. Delft University of Technology. Ph.D. defense expected 2026.
• V. Kostoulas. Leiden University. Ph.D. defense expected 2025.
• E. Sijben. Leiden University. Ph.D. defense expected 2025.
• A. Chebykin. Delft University of Technology. Ph.D. defense expected 2025.
• A. Guijt. Delft University of Technology. Ph.D. defense expected 2025.
• J. Harrison. Delft University of Technology. Ph.D. defense expected 2025.
• C. Rodriguez. Leiden University. Ph.D. defense expected 2025.
• G. Andreadis. Leiden University. Ph.D. defense expected 2025.
• L.R.M. Dickhoff. Leiden University. Ph.D. defense expected 2025.
• M. Grewal. Delft University of Technology. Ph.D. defense expected 2025.
• A. Dushatskiy. Expensive Optimization with Model-Based Evolutionary Algorithms applied to
Medical Image Segmentation using Deep Learning. Delft University of Technology. 2023.
• K.L.A. van Bindsbergen. Innovations in pediatric oncology care: Interactive tools for psycho-social
support for children with cancer and their families during treatment. University of Amsterdam. 2023.
• P.A. Bouter. Optimal Mixing Evolutionary Algorithms for Large-Scale Real-Valued Optimization -
Including Real-World Medical Applications..Delft University of Technology. 2023.
• M.C. van der Meer. Evolutionary bi-objective optimization for high-dose-rate prostate
brachytherapy with a focus on robust catheter positions. University of Amsterdam. 2022.
• Z. Wang. Radiograph-based organ dose reconstruction for childhood cancer survivors with long-term
follow-up. University of Amsterdam. 2021.
• S.C. Maree. Model-based Evolutionary Algorithms for Finding Diverse High-quality Solutions with an
Application in Brachytherapy for Prostate Cancer. University of Amsterdam. 2021.
• K. Pirpinia. Exploring the Potential and Feasibility of Multi-Objective Deformable Image Registration
for Breast Cancer Treatment. University of Amsterdam. 2020.
• K.L. Sadowski. GAMBIT - Genetic Algorithm for Model-Based mixed-Integer optimization. Utrecht
University. 2020.
• M. Virgolin. Design and Application of Gene-pool Optimal Mixing Evolutionary Algorithms for
Genetic Programming. Delft University of Technology. 2020.
• N.H. Luong. Design and Application of Scalable Evolutionary Algorithms in Electricity Distribution
Network Expansion Planning. Delft University of Technology. 2018.
• S.F. Rodrigues. A Multi-Objective Optimization Framework for the Design of Offshore Wind Farms.
Delft University of Technology. 2016.
• A.K. Hutzschenreuter. A Computational Approach to Patient Flow Logistics in Hospitals. Eindhoven
University of Technology. 2010.
Scientific Programmers & Scientific Software Developers
• Vacancy. 2027-2030.
• P.M. Matos. 2023-2026.
• P.A. Bouter. 2020-2025.
• B. Liefers. 2013-2014.
M.Sc. students
• K. Boudier. Delft University of Technology. 2025.
• S. Britton. Delft University of Technology. 2025.
• T.J. Penning. Delft University of Technology. 2025.
• M.R. I Oltra. Delft University of Technology. 2025.
• D. Toader. Delft University of Technology. 2024.
• M. Tromp. Generating Diverse Counterfactuals through Evolutionary Multi-Objective Optimization
Delft University of Technology. 2024.
• E.R. Munné. An innovative synergy between Evolutionary and Deep Learning Methods for Multi-
Objective Deformable Image Registration. Leiden University. 2024.
• N. Sweijen. Talk to GP: Explaining Genetic Programming models through natural language. Delft
University of Technology. 2024.
• C. Wever. On the Hybridization of Automated Landmark Detection with Physics-Based Multi-
Objective Deformable Image Registration with an Application to Radiotherapy. Delft University of
Technology. 2024.
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• R. Nair. MIHEA in Action: A Reproduction Study and Evaluation on Learning Bayesian Networks from
Data. Delft University of Technology. 2024.
• J. Koch. Constraint Handling in RV-GOMEA. Delft University of Technology. 2023.
• T. Moxter. Semantic Representations in Genetic Programming for Symbolic Regression: Explicit and
Model-based Perspectives on Locality. University of Amsterdam 2023.
• N. Kartoredjo. GPU-Accelerated GOMEA: Solving the max-cut problem by large-scale parallelisation
of GOMEA using GPGPU. Delft University of Technology. 2023.
• J. Mulder. Applying hybrid evolutionary algorithms to deformable image registration of 3D medical
images using common B-spline-based transformation models. Delft University of Technology. 2022.
• I. Hoogeboom. A surrogate-assisted evolutionary algorithm based on inverse distance weighting:
Applied to a multi-objective deformable image registration problem. Delft University of Technology.
2022.
• L. Everse. Neuro-GOMEA: Using Modern Evolutionary Algorithms to Train Neural Networks. Delft
University of Technology. 2022.
• M. Bakker. Warm-starting evolutionary plan optimization for high-dose-rate brachytherapy
treatment to reduce optimization time. Delft University of Technology. 2022.
• D.M.F. Ha. Hybridizing Hypervolume based Evolutionary Algorithms and Gradient Descent by
Dynamic Resource Allocation. Delft University of Technology. 2022.
• R. Scholman. Obtaining Smoothly Navigable Approximation Sets in Bi-Objective Multi-Modal
Optimization with an Application to Prostate HDR Brachytherapy Automated Treatment Planning.
Delft University of Technology. 2022.
• M. Bosma. Neural Architecture Search for Medical Image Segmentation. Delft University of
Technology. 2022.
• J. Commandeur. Improving the homogeneity of brachytherapy treatment plans generated by
BRIGHT using a hotspot registration method based on connected component analysis. Delft
University of Technology. 2021.
• T. den Ottelander. Do More Elaborate Search Strategies Lead to Better Neural Architecture Search
Performance? Delft University of Technology. 2020.
• C. Olieman. Fitness-based Linkage Learning in the Real-Valued Gene-pool Optimal Mixing
Evolutionary Algorithm. Delft University of Technology. 2019.
• E. Meulman Towards Self-Learning Model-Based Evolutionary Algorithms. Delft University of
Technology. 2019.
• P.A. Bouter. Designing the Real-Valued Gene-pool Optimal Mixing Evolutionary Algorithm and
Applying it to Substantially Improve the Efficiency of Multi-Objective Deformable Image
Registration. Delft University of Technology. 2016.
• R. de Bokx. Parallelizing the Linkage Tree Genetic Algorithm and Searching for the Optimal
Replacement for the Linkage Tree. Delft University of Technology. 2015.
• V.-L. Spiridon. A Genetic Approach to Programming Large-Scale Systems. Delft University of
Technology. 2013.
• S. van Berkel. Genetic Programming for Automatic Discovery of Algorithms for Multi-Agent Systems.
Delft University of Technology. 2011.
• J.M. Vuong. Solving the Multi-Objective Dial-a-Ride Problem Without Using Routing Heuristics. Delft
University of Technology. 2010.
• S. van Otterloo. Evolutionary Algorithms & Scheduling Problems. Utrecht University. 2001.
Research activities
Board memberships of scientific organizations
• Chair of ACM SIGEVO. 2023-2026.
• Officer of ACM SIGEVO (Secretary). 2019-2023.
• The executive board of ACM SIGEVO. 2019-2025.
• The business committee of ACM SIGEVO. 2020-2023.
Ph.D. committee member
• M. Krcek. Delft University of Technology. 2024.
• Y. Clapper. Model-Based Optimization in Home Healthcare. VU University Amsterdam. 2024.
• M. Olsthoorn. More Effective Test Case Generation with Multiple Tribes of AI. Delft University of
Technology. 2024.
• O. Pastor-Serrano. Artificial Intelligence in Radiotherapy - Probabilistic Deep Learning for Dose
Prediction and Anatomy Modeling. Delft University of Technology. 2023.
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• J. Butterworth. Manifolds & Memory - Improving the Search Speed of Evolutionary Algorithms.
University of Liverpool. 2023.
• E. Verburg. Computer-Aided Diagnosis in Screening MRI of Women with Extremely Dense Breasts.
Utrecht University. 2022.
• P. Back. Machine Learning for Optimal and Sustainable Forest Management. Aalto University. 2022.
• J.P.B. Pereira. Biology-guided algorithms - Improved cardiovascular risk prediction and biomarker
discovery. University of Amsterdam. 2022.
• E. Derner. Data-Efficient Methods for Model Learning and Control in Robotics. Czech Technical
University in Prague. 2022.
• C. Ferreira. Scheduling in Collaborative and Dynamic Environments. University of Porto. 2022.
• P. Derakhshanfar. Carving Information Sources to Drive Search-based Crash Reproduction and Test
Case Generation. Delft University of Technology. 2021.
• K. Varelas. Randomized Derivative Free Optimization via CMA-ES and Sparse Techniques -
Applications to Radars. l’Institut Polytechnique de Paris. 2021.
• C.F. Verdier. Formal synthesis of analytic controllers. An evolutionary approach. Delft University of
Technology. 2020.
• A. Lensen. Evolutionary Feature Manipulation in Unsupervised Learning. Victoria University of
Wellington. 2019.
• J. Heinerman. Better Together. VU University Amsterdam. 2019.
• O.A. Elhara. Stochastic Black-Box Optimization and Benchmarking in Large Dimension. University
Paris-Sud (Orsay). 2017.
• G. Karafotias. Parameter Control for Evolutionary Algorithms. VU University Amsterdam. 2016.
Research grant reviewer
• Hanarth fund - Artificial Intelligence in Oncology (scientific advisory board member). Since 2021.
• Netherlands Organisation for Scientific Research (NWO). Since 2016.
• Czech Science Foundation (GACR). Since 2016.
• Research Grants Council Hong Kong (RGC). Since 2010.
Journals regularly reviewed for since 2000
• Evolutionary Computation
• IEEE Transactions on Evolutionary Computation
• Swarm and Evolutionary Computation
• Natural Computing
• Soft Computing
• Applied Soft Computing
• Journal of Machine Learning Research
• IEEE Transactions on Neural Networks and Learning Systems
• IEEE Transactions on Systems, Man, and Cybernetics
Conference general chair
• The Genetic and Evolutionary Computation Conference - GECCO. 2017.
Conference local chair
• The Genetic and Evolutionary Computation Conference - GECCO. 2013.
• The International Conference on Computational Management Science - CMS. 2006.
Conference programme chair
• The CO track at the Genetic and Evolutionary Computation Conference - GECCO. 2016.
• The EDA track at the Genetic and Evolutionary Computation Conference - GECCO. 2006, 2009.
• The late-breaking papers track at the Genetic and Evolutionary Computation Conference - GECCO. 2007.
Conference programme committee member
• The Genetic and Evolutionary Computation Conference - GECCO. Since 2002.
• The Parallel Problem Solving from Nature conference - PPSN. Since 2004.
• The IEEE Congress on Evolutionary Computation - CEC. Since 2006.
• The BeNelux Artificial Intelligence Conference - BNAIC. 2013-2017.
• The European Conference on Applications of Evolutionary Computation - EvoApplications. 2008-2012.
• The International Conference on Smart Grids and Green IT Systems - SMARTGREENS. 2013, 2014.
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Symposium chair
• “Challenges in Benchmarking Optimization Heuristics”. Dagstuhl Seminar. 2023.
• “Back to the Future and Beyond: Traversing the Ever-Evolving Landscape of Evolutionary Algorithms”.
International Scientific Symposium on State-of-the-Art Evolutionary Computation Approaches and
their (Medical) Applications, held at Delft University of Technology. 2019.
Workshop chair
• The Analysing Algorithmic Behaviour of Optimisation Heuristics Workshop (AABOH) at the Genetic and
Evolutionary Computation Conference - GECCO. 2022.
• The Green and Efficient Energy Applications of Genetic and Evolutionary Computation workshop -
GreenGEC at the Genetic and Evolutionary Computation Conference - GECCO. 2012, 2013, 2014.
• The Evolutionary Algorithms for Dynamic Optimization Problems workshop - EvoDOP at the Genetic
and Evolutionary Computation Conference - GECCO. 2007.
• The Optimization by Building and Using Probabilistic Models workshop - OBUPM at the Genetic and
Evolutionary Computation Conference - GECCO. 2006.
Workshop programme committee member
• The Investigating Optimization Problems from Machine Learning and Data Analysis workshop at the
Parallel Problem Solving from Nature conference. 2018.
• The IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments - CIDUE.
2009, 2013.
• The Optimization by Building and Using Probabilistic Models workshop - OBUPM at the Genetic and
Evolutionary Computation Conference - GECCO. 2000, 2001.
Book review board member
• Springer volume Natural Intelligence for Scheduling, Planning and Packing Problems. 2009.
Tutorial presenter
• The Model-Based Evolutionary Algorithms tutorial at the Genetic and Evolutionary Computation
Conference - GECCO. Since 2013.
Keynotes
• At the International Symposium on Late Complications After Childhood Cancer (ISLCCC). 2022.
Invited talks
• At the TU Delft Faculty lunch. 2024.
• At the Dutch Olympiad ceremony. 2023.
• At IKEA AI Summit. 2023
• At KWF Talks. 2023.
• At the Head and Neck Symposium of the University Medical Center Utrecht. 2023.
• At the Analytics and optimization seminar at VU Amsterdam. 2022.
• At the “AI in Oncology” seminar at TU Delft. 2022.
• At Philips. 2021.
• At Booking.com. 2021.
• At the Masterclass series of the Delft University Fund. 2021.
• At the symposium on ``Machine learning, artificial intelligence and big data for precision medicine'' at
the World Congress of Brachytherapy. 2021.
• At the 75
th
anniversary celebration of CWI. 2021.
• At the Delft AI meetup. 2019.
• At the AMDS/ADS (Amsterdam Medical Data Science / Amsterdam Data Science) meetup on
"Personalization in Health". 2019.
• At the symposium on "Inverse planning in brachytherapy - A one click solution?" at the European
Society for Radiotherapy and Oncology (ESTRO) conference. 2019.
• At the Victoria University of Wellington, New Zealand. 2018.
• At the Queensland University, Australia. 2018.
• At the Royal Melbourne Institute of Technology. 2018.
• At the University of New South Wales, Australia. 2018.
• At the University of Adelaide, Australia. 2018.
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• At the TAU and RANDOPT research groups of INRIA Saclay, Paris, France. 2018.
• At the Health Track (plenary talk) in the Conference for ICT-Research in the Netherlands (ICT.OPEN). 2018.
• At the AMC Radiation Oncology reference meeting. 2018.
• At the department research meeting of the Medical Informatics department at the Academic Medical
Center (AMC). 2018.
• At the seminar of the AI section of the Department of Computer Sciences of the Vrije Universiteit
Amsterdam. 2018.
• At the bi-annual RKF (Radiotherapeutische Klinische Fysica) Scientific Project Day. 2017.
• At the Model-Based Evolutionary Algorithms - MBEA workshop at the Genetic and Evolutionary
Computation Conference - GECCO. 2017.
• At the seminar of the ALICE research group at the University of Groningen. 2017.
• At the IPA Herfstdagen on "Algorithms and Models for Real-Life Systems". 2015.
• At the physics seminar of the department of Radiation Oncology at the Amsterdam Medical Center
(AMC). 2013.
• At the minisymposium Issues of Modeling & Uncertainty in Simulation-Based Applications of
Optimization at the SIAM Conference on Optimization. 2011.
• At the Bridging the Gap (BTG) Workshop 7 on Dynamic Optimisation in an Uncertain World: Challenges
and State-of-the-Art. 2011.
• At the Optimization by Building and Using Probabilistic Models - OBUPM workshop at the Genetic and
Evolutionary Computation Conference - GECCO. 2005, 2006, 2010.
• At the ORMS seminar of Warwick Business School. 2010.
• At the seminar on Operations Research of CentER (business & economics research insitute of Tilburg
University). 2009.
• At the Dagstuhl Seminar Sampling-based Optimization in the Presence of Uncertainty. 2009
• At the IPA Herfstdagen on "Beyond Turing". 2007.
• At the seminar of the Department of Logistics, University of Mannheim. 2006.
International visits and visitors
• 1-week visit at CWI by Dr. S. Thomson from Napier University, UK. 2023.
• 3-day visit at CWI by Prof.dr. R. Miikkulainen from University of Texas at Austin, USA. 2023.
• 3-day visit at CWI by Dr. M. Gallagher from University of Queensland, Australia. 2023.
• 1-week visit at CWI by Dr. A. Lensen from Victoria University of Wellington, New Zealand. 2023.
• 3-day visit at CWI by Prof.dr. D. Todor from Virginia Commonwealth University. 2023.
• 2-day visit to Prof.dr. M. Zhang and Dr. B. Xue at Victoria University of Wellington, New Zealand. 2018.
• 2-day visit to Dr. M. Gallagher at University of Queensland, Australia. 2018.
• 2-day visit to Prof.dr. H. Abbass at University of New South Wales Canberra, Australia. 2018.
• 2-day visit to Prof.dr. X. Li at Royal Melbourne Institute of Technology, Australia. 2018.
• 2-day visit to Dr. M. Wagner and Prof.dr. F. Neumann at University of Adelaide, Australia. 2018.
• 1-day visit to Dr. M. Schoenauer, Dr. N. Hansen, and Dr. Anne Auger at INRIA, France. 2018.
• 4-day visit at CWI by Prof.dr. K. Deb from Michigan State University, USA. 2018.
• 1-week visit at CWI by Dr. M. Gallagher from University of Queensland, Australia. 2015.
• 1-week visit at CWI by M.Sc. R. Morgan from University of Queensland, Australia. 2014.
• 3-day visit at CWI by Dr. G. Gray from Sandia National Laboratories, USA. 2012.
• 1-week visit to Prof.dr. J. Branke at Warwick Business School, Coventry, UK. 2010.
• 1-week visit to Prof.dr. S. Minner and M.Sc. J. Grahl, Mannheim, Germany. 2006.
Appearances in popular media
• “Hoe komt AI tot zijn keuzes? Wetenschap kraakt de black box”. KIJK Magazine. 2024
• “DTL S7A3 - Evolutionaire Intelligentie: de volgende stap in AI”. De Dataloog podcast. 2023.
• “Algorithms that work”. Smart Health Amsterdam. 2022.
• “How to find funds”. I/O magazine, 2021.
• “Opsporen en behandelen: het voortdurende gevecht tegen kanker”. NWO resultaat. 2021.
• “Kunstmatige Intelligentie: concurrentie of aanvulling voor de arts?”. BNR Nieuwsradio, Beter. 2021.
• “Welke grote sprongen kunnen we nog maken met wiskunde en informatica?”. NOS Met het Oog op
Morgen (NPO Radio 1). 2021.
• “Doelen en drijfveren: liever mensen dan geld of objecten”. De ingenieur, 2020.
• “Waar blijft de mens?”. NWO Onderzoek, 2020.
• “Eerste patiënt behandeld met bestralingsplan van AI”. Technisch Weekblad, 2020.
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• “Doctors embrace AI: computer calculates best radiation treatment”. Bits & Chips, 2020.
• “Beter bestralen dankzij kunstmatige intelligentie”. De ingenieur, 2020.
• “Hoe helpen wiskunde en informatica bij het bestralen van kanker?”. YouTube video as part of NWO
(Dutch national science foundation) outreach campaign pertaining to its scientific institutes. 2018.
• “Efficiënter kanker bestralen met algoritmes”. BNR Nieuwsradio, Hemmen. 2018.
• “Robotvriendje helpt kind met kanker”. Multiple news outlets, including NU.nl, RTLNieuws.nl, and
Metronieuws.nl, 2017.
• “Robotje Marv moet kind met kanker bij gaan staan”. Het Parool, 2017.
• Onderzoeker van de week op website van KWF, 2017.
• “Buddy in de behandelkamer”. I/O magazine, 2017.
• “Een robot die kan troosten”. AMC magazine, 2016.
• “Informatici verbeteren bestralingstherapie”. Technisch weekblad, 2015.
• “Betere ICT voor betere bestraling”. I/O magazine, 2015.
• “CWI verbetert ICT voor kankerbestraling”. Computable, 2015.
• “CWI verbetert IT achter bestraling van kankerpatiënten”. Automatiseringsgids, 2015.
• “Beter door Informatica”. AMC magazine, 2015.
Project manager/leader besides projects pertaining to own research grants
• BSIK (ICES/KIS-3) project: Basic Research in Informatics for Creating the Knowledge Society - BRICKS.
2008, 2009, 2010.
• BRICKS project: Decision Support Systems for Logistic Networks and Supply Chain Optimization (IS3).
2008, 2009.
Educational activities
(Co-)lecturer
• Ph.D. course Basic Course Algorithms and Complexity (full day) at IPA research school. 2007, 2010,
2013, 2016.
• Ph.D. course Computational Intelligence (half day) at SIKS research school. 2006, 2009.
• M.Sc. course Evolutionary Algorithms at Delft University of Technology. Since 2019.
• M.Sc. course Literature Study at Delft University of Technology. 2015.
• M.Sc. course Combinatorial Solvers at Delft University of Technology. 2013, 2014.
• M.Sc. course Advanced Algorithms at Delft University of Technology. 2009-2012.
• M.Sc. course Statistical Learning at Utrecht University. 2002, 2003.
• B.Sc. course Bachelor Seminar at Delft University of Technology. 2009-2018.
• B.Sc. course Search Algorithms at Utrecht University. 2003.
• B.Sc. course Software Project at Utrecht University. 2002, 2003.
Guest lecturer
• M.Sc. course Evolutionary Algorithms at Utrecht University. 2009-2013.
• M.Sc. course Evolutionary Algorithms at Radboud University Nijmegen. 2009.
• B.Sc. course Representing and Searching at Utrecht University. 2002.
Assistant lecturer
• M.Sc. course Evolutionary Algorithms at Utrecht University. 1998-2001.
• B.Sc. course Datastructures at Utrecht University. 2000.
• B.Sc. course Algorithms \& Datastructures II at Utrecht University. 1999.
• B.Sc. course Formal Methods at Utrecht University. 1998.
Student Teaching Assistant
• 10 various homework sessions (werkcolleges) and practical works (practica) for courses Imperative
Programming, Implementation of Programming Languages, Program Composition and Evolutionary
Algorithms at Utrecht University. 1995-1998.
Promotional committee member
• The information-for-candidate-students committee for Computer Science and Information Science at
Utrecht University. 2003.
Student Promotional Assistant
• Information for candidate students on information days at Utrecht University. 1995-1998.
