Prof.dr. Peter A.N. Bosman
Senior Researcher at Centrum Wiskunde & Informatica (CWI)
(Dutch National Research Institute for Mathematics and Computer Science)
Life Sciences and Health Research Group

Professor of Evolutionary Algorithms at Delft University of Technology
Algorithmics Section of the Department of Software Technology
Faculty of Electrical Engineering, Mathematics and Computer Science

E Peter.Bosman@cwi.nl    W http://homepages.cwi.nl/~bosman    T +31(0)20 592 4265
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Publications

To appear (accepted for publication)
[1] S.C. Maree, P.A.N. Bosman, N. van Wieringen, Y. Niatsetski, B.R. Pieters, A. Bel, and T. Alderliesten. Automatic bi-objective parameter tuning for inverse planning of high-dose-rate prostate brachytherapy. In Physics in Medicine and Biology.
[2] M. Virgolin, T. Alderliesten, C. Witteveen, and P.A.N. Bosman. Improving Model-based Genetic Programming for Symbolic Regression of Small Expressions. In Evolutionary Computation.
[3] Z. Wang, M. Virgolin, P.A.N. Bosman, K.F. Crama, B.V. Balgobind, A. Bel, and T. Alderliesten. Automatic generation of 3D dose reconstruction data for 2D radiotherapy plans for historically treated patients. In Journal of Medical Imaging.
[4] M. Virgolin, T. Alderliesten, and P.A.N. Bosman. On Explaining Machine Learning Models by Evolving Crucial and Compact Features. In Swarm and Evolutionary Computation.
[5] M.C. van der Meer, D. van Dorth, P.A.N. Bosman, B.R. Pieters, Y. Niatsetski, T. Alderliesten, and A. Bel. Healthy tissue constraints for catheter position optimization in HDR prostate brachytherapy planning. At The European SocieTy for Radiotherapy & Oncology conference - ESTRO-2020. 2020.
[6] A. Bouter, T. Alderliesten, B.R. Pieters, S. Buus, A. Bel, Y. Niatsetski, and P.A.N. Bosman. A multi-protocol validation study of automated bi-objective planning for HDR prostate brachytherapy. At The European SocieTy for Radiotherapy & Oncology conference - ESTRO-2020. 2020.
[7] M. Virgolin, Z. Wang, B.V. Balgobind, I.W.E.M. van Dijk, J. Wiersma, D.C. Hodgson, A. Bryce-Atkinson, M. van Herk, C.R.N. Rasch, L.Z. Zaletel, P.S. Kroon, G.O. Janssens, A. Bel, P.A.N. Bosman, and T. Alderliesten. Highly-individualized dose reconstruction for pediatric abdominal radiotherapy with machine learning. At The European SocieTy for Radiotherapy & Oncology conference - ESTRO-2020. 2020.
[8] A. Dushatskiy, A.M. Mendrik, P.A.N. Bosman, and T. Alderliesten Observer variation-aware medical image segmentation by combining deep learning and surrogate-assisted genetic algorithms. In Proceedings of the SPIE Medical Imaging Conference 2020. SPIE, Bellingham, WA, 2020.
[9] M. Virgolin, Z. Wang, T. Alderliesten, and P.A.N. Bosman. Machine learning for automatic construction of pediatric abdominal phantoms for radiation dose reconstruction. In Proceedings of the SPIE Medical Imaging Conference 2020. SPIE, Bellingham, WA, 2020.
[10] M. Grewal, T.M. Deist, J. Wiersma, P.A.N. Bosman, and T. Alderliesten. An unsupervised deep learning approach for landmark detection and matching in medical images. In Proceedings of the SPIE Medical Imaging Conference 2020. SPIE, Bellingham, WA, 2020.
[11] N.H. Luong, T. Alderliesten, B.R. Pieters, A. Bel, Y. Niatsetski, and P.A.N. Bosman. Fast and insightful bi-objective optimization for prostate cancer treatment planning with high-dose-rate brachytherapy. In Applied Soft Computing. 2019.
[12] A. Bouter, T. Alderliesten, B.R. Pieters, A. Bel, Y. Niatsetski, and P.A.N. Bosman. GPU-Accelerated Bi-Objective Treatment Planning for Prostate High-Dose-Rate Brachytherapy. In Medical Physics. 2019.
[13] M.C. van der Meer, P.A.N. Bosman, B.R. Pieters, Y. Niatsetski, T. Alderliesten, and A. Bel. Robust HDR prostate brachytherapy planning accounting for organ reconstruction settings. At The European SocieTy for Radiotherapy & Oncology conference - ESTRO-2019. 2019.
[14] K. Pirpinia, P.A.N. Bosman, J.-J. Sonke, M. van Herk, and T. Alderliesten. Evolutionary Machine Learning for Multi-Objective Class Solutions in Medical Deformable Image Registration . In Algorithms. 12(5), 99, 2019.
[15] 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. At The European SocieTy for Radiotherapy & Oncology conference - ESTRO-2019. 2019.
[16] Z. Wang, B.V. Balgobind, M. Virgolin, I.W.E.M. van Dijk, J. Wiersma, C.M. Ronckers, P.A.N. Bosman, A. Bel, and T. Alderliesten How do patient characteristics and anatomical features correlate to accuracy of organ dose reconstruction for Wilms’ tumor radiation treatment plans when using a surrogate patient’s CT scan?. In Journal of Radiological Protection. 39(2), pages 598-619, 2019.
[17] S.C. Maree, T. Alderliesten, and P.A.N. Bosman. Real-valued Evolutionary Multi-modal Multi-objective Optimization by Hill-valley Clustering In M. López-Ibáñez et al., editors, Proceedings of the Genetic and Evolutionary Computation Conference - GECCO-2019, pages 568-576, ACM Press, New York, New York, 2019.
[18] M. Virgolin, T. Alderliesten, and P.A.N. Bosman. Linear Scaling with and within Semantic Backpropagation-based Genetic Programming for Symbolic Regression In M. López-Ibáñez et al., editors, Proceedings of the Genetic and Evolutionary Computation Conference - GECCO-2019, pages 1084-1092, ACM Press, New York, New York, 2019.
[19] A. Dushatskiy, A.M. Mendrik, T. Alderliesten, and P.A.N. Bosman. Convolutional Neural Network Surrogate-Assisted GOMEA In M. López-Ibáñez et al., editors, Proceedings of the Genetic and Evolutionary Computation Conference - GECCO-2019, pages 753-761, ACM Press, New York, New York, 2019.
[20] E.A. Meulman, and P.A.N. Bosman. Toward Self-Learning Model-Based EAs In E. Hart et al., organisers, Evolutionary Computation for the Automated Design of Algorithms ECADA Workshop at the Genetic and Evolutionary Computation Conference - GECCO-2019, pages 1495-1503, ACM Press, New York, New York, 2019.
[21] M.C. van der Meer, A. Bouter, B.R. Pieters, Y. Niatsetski, T. Alderliesten, P.A.N. Bosman, and A. Bel. GPU parallelization of catheter position optimization for HDR prostate brachytherapy. At The International Conference on the use of Computers in Radiation Therapy - ICCR-2019, 2019.
[22] 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 prostatebrachytherapy – A retrospective observer study. In Brachytherapy. 18(3), pages 396-403, 2019.
[23] 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.
[24] Z. Wang, M. Virgolin, P.A.N. Bosman, B.V. Balgobind, A. Bel, and T. Alderliesten. Automatic radiotherapy plan emulation for 3D dose reconstruction to enable big data analysis for historically treated patients. In Proceedings of the SPIE Medical Imaging Conference 2019. 109540V; doi:10.1117/12.2512758, SPIE, Bellingham, WA, 2019.
[25] M.C. van der Meer, P.A.N. Bosman, B.R. Pieters, Y. Niatsetski, N. van Wieringen, T. Alderliesten, and A. Bel. Sensitivity of dose-volume indices to computation settings in high-dose-rate prostate brachytherapy treatment plan evaluation. In J Appl Clin Med Phys. 20(4), pages 66-74, 2019.
[26] N.H. Luong, P.A.N. Bosman, M.O.W. Grond, and H. La Poutré. Evolutionary Multi-Objective Dynamic Distribution Network Expansion Planning With Demand Side Management. In K.Y. Lee and Z. Vale, editors, Application of Modern Heuristic Optimization Methods in Power and Energy Systems.

Articles in international journals
[27] K. Pirpinia, P.A.N. Bosman, C.E. Loo, N.S. Russell, M.B. van Herk, and T. Alderliesten. Simplex-based navigation tool for a posteriori selection of the preferred deformable image registration outcome from a set of trade-off solutions obtained with multi-objective optimization for the case of breast MRI. In Journal of Medical Imaging 5(4), 045501; doi:10.1117/1.JMI.5.4.045501, 2018.
[28] E. Medvet, M. Virgolin, M. Castelli, P.A.N. Bosman, I. Gonçalves, and T. Tušar. Unveiling evolutionary algorithm representation with DU maps. In Genetic Programming and Evolvable Machines 19(3), pages 351–389, 2018.
[29] Z. Wang, I.W.E.M. van Dijk, J. Wiersma, C.M. Ronckers, F. Oldenburger, B.V. Balgobind, P.A.N. Bosman, A. Bel, and T. Alderliesten. Are age and gender suitable matching criteria in organ dose reconstruction using surrogate childhood cancer patients' CT scans? In Medical Physics 45(6), pages 2628-2638, 2018.
[30] N.H. Luong, H. La Poutré, and P.A.N. Bosman. Multi-objective Gene-pool Optimal Mixing Evolutionary Algorithm with the Interleaved Multi-start Scheme. In Swarm and Evolutionary Computation 40, pages 238-254, 2018.
[31] M. Virgolin, I.W.E.M. van Dijk, J. Wiersma, C.M. Ronckers, C. Witteveen, A. Bel, T. Alderliesten, and P.A.N. Bosman. On the feasibility of automatically selecting similar patients in highly individualized radiotherapy dose reconstruction for historic data of pediatric cancer survivors. In Medical Physics 45(4), pages 1504-1517, 2018.
[32] N.H. Luong, T. Alderliesten, A. Bel, Y. Niatsetski, and P.A.N. Bosman. Application and Benchmarking of Multi-Objective Evolutionary Algorithms on High-Dose-Rate Brachytherapy Planning for Prostate Cancer Treatment. In Swarm and Evolutionary Computation 40, pages 37-52, 2018.
[33] P.A.N. Bosman and M. Gallagher. The Importance of Implementation Details and Parameter Settings in Black-Box Optimization: a Case Study on Gaussian Estimation-of-Distribution Algorithms and Circles-in-a-Square Packing Problems. In Soft Computing, 22(4), pages 1209-1223, 2018.
[34] K. Pirpinia, P.A.N. Bosman, C.E. Loo, G. Winter-Warnars, N.N.Y. Janssen, A.N. Scholten, J.-J. Sonke, M. van Herk, and T. Alderliesten. The Feasibility of Manual Parameter Tuning for Deformable Breast MR Image Registration from a Multi-Objective Optimization Perspective. In Physics in Medicine and Biology 62(14), pages 5723-5743, 2017.
[35] 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 Evolutionary Computation, http://dx.doi.org/10.1162/evco_a_00209, First online: April 07, 2017.
[36] K.L. Sadowski, D. Thierens, and P.A.N. Bosman. GAMBIT: A Parameterless Model-Based Evolutionary Algorithm for Mixed-Integer Problems. In Evolutionary Computation, http://dx.doi.org/10.1162/evco_a_00206, First online: February 16, 2017.
[37] S.F. Rodrigues, P. Bauer, and P.A.N. Bosman. Multi-Objective Optimization of Wind Farm Layouts - Complexity, Constraint Handling and Scalability. In Renewable & Sustainable Energy Reviews 65, pages 587-609, 2016.
[38] S.F. Rodrigues, C. Restrepo, G. Katsouris, R.T. Pinto, M. Soleimanzadeh, P.A.N. Bosman, and P. Bauer. A Multi-Objective Optimization Framework for Offshore Wind Farm Layouts and Electric Infrastructures. In Energies 9(3), 216, 2016.
[39] S.F. Rodrigues, R.T. Pinto, M. Soleimanzadeh, P.A.N. Bosman, and P. Bauer. Wake Losses Optimization of Offshore Wind Farms with Moveable Floating Wind Turbines. In Energy Conversion and Management 89, pages 933-941, 2015.
[40] P.A.N. Bosman, J. Grahl, and D. Thierens. Benchmarking Parameter-free AMaLGaM on Functions With and Without Noise . In Evolutionary Computation 21(3), pages 445-469, 2013.
[41] P.A.N. Bosman. On Gradients and Hybrid Evolutionary Algorithms for Real-Valued Multi-Objective Optimization. In IEEE Transactions on Evolutionary Computation 16(1), pages 51-69, 2012.
[42] P.A.N. Bosman and J. Grahl. Matching Inductive Search Bias and Problem Structure in Continuous Estimation-of-Distribution Algorithms. In European Journal of Operational Research 185(3), pages 1246-1264, 2008.
[43] T. Alderliesten, P.A.N. Bosman, and W. Niessen. Towards a Real-Time Minimally-Invasive Vascular Intervention Simulation System. In IEEE Transactions on Transactions on Medical Imaging 26(1), pages 128-132, 2007.
[44] P.A.N. Bosman and D. Thierens. The Balance Between Proximity and Diversity in Multiobjective Evolutionary Algorithms. In IEEE Transactions on Evolutionary Computation 7(2), pages 174-188, 2003.
[45] P.A.N. Bosman and D. Thierens. Multi-objective Optimization with Diversity Preserving Mixture-based Iterated Density Estimation Evolutionary Algorithms. In International Journal of Approximate Reasoning, 31(3), pages 259-289, 2002.

Papers in international conference proceedings
[46] G. Aalvanger, H.N. Luong, P.A.N. Bosman and D. Thierens. Heuristics in Permutation GOMEA for Solving the Permutation Flowshop Scheduling Problem. In A. Auger et al., editors, Parallel Problem Solving from Nature - PPSN XIV, pages 146-157, Springer-Verlag, Berlin, 2018.
[47] A. Bouter, T. Alderliesten, A. Bel, C. Witteveen, and P.A.N. Bosman. Large-Scale Parallelization of Partial Evaluations in Evolutionary Algorithms for Real-World Problems. In H. Aguirre et al., editors, Proceedings of the Genetic and Evolutionary Computation Conference - GECCO-2018, pages 1199-1206, ACM Press, New York, New York, 2018.
[48] M. Virgolin, T. Alderliesten, A. Bel, C. Witteveen, and P.A.N. Bosman. Symbolic Regression and Feature Construction with GP-GOMEA applied to Radiotherapy Dose Reconstruction of Childhood Cancer Survivors. In H. Aguirre et al., editors, Proceedings of the Genetic and Evolutionary Computation Conference - GECCO-2018, pages 1395-1402, ACM Press, New York, New York, 2018.
[49] 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.
[Nominated for Best Paper Award]
[50] M.C. van der Meer, B.R. Pieters, Y. Niatsetski, T. Alderliesten, A. Bel, and P.A.N. Bosman. Better and Faster Catheter Position Optimization in HDR Brachytherapy for Prostate Cancer using Multi-Objective Real-Valued GOMEA. In H. Aguirre et al., editors, Proceedings of the Genetic and Evolutionary Computation Conference - GECCO-2018, pages 1387-1394, ACM Press, New York, New York, 2018.
[51] N.H. Luong, T. Alderliesten, and P.A.N. Bosman. Improving the Performance of MO-RV-GOMEA on Problems with Many Objectives using Tchebycheff Scalarizations. In H. Aguirre et al., editors, Proceedings of the Genetic and Evolutionary Computation Conference - GECCO-2018, pages 705-712, ACM Press, New York, New York, 2018.
[52] S.C. Maree, T. Alderliesten, D. Thierens, and P.A.N. Bosman. Real-Valued Evolutionary Multi-Modal Optimization driven by Hill-Valley Clustering. In H. Aguirre et al., editors, Proceedings of the Genetic and Evolutionary Computation Conference - GECCO-2018, pages 857-864, ACM Press, New York, New York, 2018.
[53] A. Bouter, N.H. Luong, C. Witteveen, T. Alderliesten, and P.A.N. Bosman. The multi-objective 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 537-544, ACM Press, New York, New York, 2017.
[54] A. 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.
[Nominated for Best Paper Award]
[55] M. Virgolin, T. Alderliesten, C. Witteveen, and P.A.N. Bosman. Scalable genetic programming by gene-pool optimal mixing and input-space entropy-based building block learning. In G. Ochoa et al., editors, Proceedings of the Genetic and Evolutionary Computation Conference - GECCO-2017, pages 1041-1048, ACM Press, New York, New York, 2017.
[56] S.C. Maree, T. Alderliesten, D. Thierens, and P.A.N. Bosman. Niching an estimation-of-distribution algorithm by hierarchical Gaussian mixture learning. In G. Ochoa et al., editors, Proceedings of the Genetic and Evolutionary Computation Conference - GECCO-2017, pages 713-720, ACM Press, New York, New York, 2017.
[57] K.L. Sadowski, M.C. van der Meer, N.H. Luong, T. Alderliesten, D. Thierens, R. van der Laarse, Y. Niatsetski, A. Bel, and P.A.N. Bosman. Exploring trade-offs between target coverage, healthy tissue sparing, and the placement of catheters in HDR brachytherapy for prostate cancer using a novel multi-objective model-based mixed-integer evolutionary algorithm. In G. Ochoa et al., editors, Proceedings of the Genetic and Evolutionary Computation Conference - GECCO-2017, pages 1224-1231, ACM Press, New York, New York, 2017.
[58] P.A. Bouter, T. Alderliesten, and P.A.N. Bosman. A novel model-based evolutionary algorithm for multi-objective deformable image registration with content mismatch and large deformations: benchmarking efficiency and quality. In M.A. Styner and E.D. Angelini, editors, Proceedings of the SPIE Medical Imaging Conference 2017. 1013312; doi:10.1117/12.2254144, SPIE, Bellingham, WA, 2017.
[59] W. den Besten, D. Thierens, and P.A.N. Bosman. The Multiple Insertion Pyramid: A Fast Parameter-less Population Scheme. In J. Handl et al., editors, Parallel Problem Solving from Nature - PPSN XIV, pages 48-58, Springer-Verlag, Berlin, 2016.
[60] P.A.N. Bosman, N.H. Luong, and D. Thierens. Expanding from Discrete Cartesian to Permutation Gene-pool Optimal Mixing Evolutionary Algorithms. In T. Friedrich et al., editors, Proceedings of the Genetic and Evolutionary Computation Conference - GECCO-2016, pages 637-644, ACM Press, New York, New York, 2016.
[Nominated for Best Paper Award]
[61] K.L. Sadowski, P.A.N. Bosman, and D. Thierens. Learning and Exploiting Mixed Variable Dependencies with a Model-Based EA. In Proceedings of the IEEE Congress on Evolutionary Computation - CEC-2016. pages 4382-4389, IEEE Press, Piscataway, New Jersey, 2016.
[62] P.A.N. Bosman and T. Alderliesten. Smart grid initialization reduces the computational complexity of multi-objective image registration based on a dual-dynamic transformation model to account for large anatomical differences. In M.A. Styner and E.D. Angelini, editors, Proceedings of the SPIE Medical Imaging Conference 2016. 978447; doi:10.1117/12.2217011, SPIE, Bellingham, WA, 2016.
[63] K. Pirpinia, P.A.N. Bosman, J.-J. Sonke, M. van Herk, and T. Alderliesten. A first step toward uncovering the truth about weight tuning in deformable image registration. In M.A. Styner and E.D. Angelini, editors, Proceedings of the SPIE Medical Imaging Conference 2016. 978445; doi:10.1117/12.2216370, SPIE, Bellingham, WA, 2016.
[64] K. Pirpinia, T. Alderliesten, J.-J. Sonke, M. van Herk, and P.A.N. Bosman. Diversifying Multi-Objective Gradient Techniques and their Role in Hybrid Multi-Objective Evolutionary Algorithms for Deformable Medical Image Registration. In S. Silva and A.I. Esparcia-Alcázar, editors, Proceedings of the Genetic and Evolutionary Computation Conference - GECCO-2015, pages 1255-1262, ACM Press, New York, New York, 2015.
[65] 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 and A.I. Esparcia-Alcázar, editors, Proceedings of the Genetic and Evolutionary Computation Conference - GECCO-2015, pages 1231-1238, ACM Press, New York, New York, 2015.
[Winner of Best Paper Award]
[66] K.L. Sadowski, P.A.N. Bosman, and D. Thierens. A Clustering-Based Model-Building EA for Optimization Problems with Binary and Real-Valued Variables. In S. Silva and A.I. Esparcia-Alcázar, editors, Proceedings of the Genetic and Evolutionary Computation Conference - GECCO-2015, pages 911-918, ACM Press, New York, New York, 2015.
[67] R. de Bokx, D. Thierens, and P.A.N. Bosman. In Search of Optimal Linkage Trees. In S. Silva and A.I. Esparcia-Alcázar, editors, Proceedings of the Genetic and Evolutionary Computation Conference - GECCO-2015, pages 1375-1376, ACM Press, New York, New York, 2015.
[68] N.H. Luong, M.O.W. Grond, H. La Poutré, and P.A.N. Bosman. Scalable and Practical Multi-Objective Distribution Network Expansion Planning. In Proceedings of the 2015 IEEE PES General Meeting, IEEE Press, Piscataway, New Jersey, 2015.
[69] T. Alderliesten, P.A.N. Bosman, and A. Bel. Getting the most out of additional guidance information in deformable image registration by leveraging multi-objective optimization. In S. Ourselin and M.A. Styner, editors, Proceedings of the SPIE Medical Imaging Conference 2015. 94131R; doi:10.1117/12.2081438, SPIE, Bellingham, WA, 2015.
[70] K. Pirpinia, P.A.N. Bosman, J.-J. Sonke, M.B. van Herk, and T. Alderliesten. On the usefulness of gradient information in multi-objective deformable image registration using a B-spline-based dual-dynamic transformation model: comparison of three optimization algorithms. In S. Ourselin and M.A. Styner, editors, Proceedings of the SPIE Medical Imaging Conference 2015. 941339; doi:10.1117/12.2082172, SPIE, Bellingham, WA, 2015.
[71] 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 XIII, pages 342-351, Springer-Verlag, Berlin, 2014.
[Nominated for Best Paper Award]
[72] M.O.W. Grond, N.H. Luong, J. Morren, P.A.N. Bosman, H. Slootweg, and H. La Poutré, Practice-oriented Optimization of Distribution Network Planning Using Metaheuristic Algorithms. In Proceedings of the Power Systems Computation Conference - PSCC-2014, IEEE Press, Piscataway, New Jersey, 2014.
[73] N.H. Luong, H. La Poutré, and P.A.N. Bosman, Multi-objective Gene-pool Optimal Mixing Evolutionary Algorithms. In D.V. Arnold, editor, Proceedings of the Genetic and Evolutionary Computation Conference - GECCO-2014, pages 357-364, ACM Press, New York, New York, 2014.
[74] S. Rodrigues, P. Bauer, and P.A.N. Bosman. A Novel Population-based Multi-Objective CMA-ES and the Impact of Different Constraint Handling Techniques. In D.V. Arnold, editor, Proceedings of the Genetic and Evolutionary Computation Conference - GECCO-2014, pages 991-998, ACM Press, New York, New York, 2014.
[75] B. Liefers, F. Claessen, E. Pauwels, P.A.N. Bosman, and H. La Poutré. Market Garden: a Simulation Environment for Research and User Experience in Smart Grids. In Y. Demazeau et al., editors, Proceedings of the 12th International Conference on Practical Applications of Agents and Multi-Agent Systems - PAAMS'14, pages 351-354, Springer-Verlag, Berlin, 2014.
[76] S. Rodrigues, P. Bauer, P.A.N. Bosman, and J. Pierik. Collection Network Cable Routing and Wake Losses Optimization in Offshore Wind Farms. In Proceedings of the Symposium of Specialists in Electric Operational and Expansion Planning - SEPOPE-XIII, Foz do Iguassu, Brasil, 2014.
[77] T. Alderliesten, P.A.N. Bosman, J.-J. Sonke, and A. Bel. A multi-resolution strategy for a multi-objective deformable image registration framework that accommodates large anatomical differences. In S. Ourselin and M.A. Styner, editors, Proceedings of the SPIE Medical Imaging Conference 2014, 90343G; doi:10.1117/12.2042856, SPIE, Bellingham, WA, 2014.
[78] N.H. Luong, M.O.W. Grond, P.A.N. Bosman, and H. La Poutré. Medium-Voltage Distribution Network Expansion Planning with Gene-pool Optimal Mixing Evolutionary Algorithms. In P. Legrand et al., editors, Proceedings of the Evolution Artificielle Conference - EA-2013, pages 93-105, Springer-Verlag, Berlin, 2014.
[79] P.A.N. Bosman and D. Thierens. More Concise and Robust Linkage Learning by Filtering and Combining Linkage Hierarchies. In C. Blum and E. Alba, editors, Proceedings of the Genetic and Evolutionary Computation Conference - GECCO-2013, pages 359-366, ACM Press, New York, New York, 2013.
[80] D. Thierens and P.A.N. Bosman. Hierarchical Problem Solving with the Linkage Tree Genetic Algorithm. In C. Blum and E. Alba, editors, Proceedings of the Genetic and Evolutionary Computation Conference - GECCO-2013, pages 877-884, ACM Press, New York, New York, 2013.
[81] K.L. Sadowski, P.A.N. Bosman, and D. Thierens. On the usefulness of linkage processing for solving MAX-SAT. In C. Blum and E. Alba, editors, Proceedings of the Genetic and Evolutionary Computation Conference - GECCO-2013, pages 853-860, ACM Press, New York, New York, 2013.
[82] 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 and E. Alba, editors, Proceedings of the Genetic and Evolutionary Computation Conference - GECCO-2013, pages 1125-1132, ACM Press, New York, New York, 2013.
[Winner of Best Paper Award]
[83] 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.
[Nominated for Best Paper Award]
[84] T. Alderliesten, J.-J. Sonke, and P.A.N. Bosman. Deformable image registration by multi-objective optimization using a dual-dynamic transformation model to account for large anatomical differences. In D. R. Haynor and S. Ourselin, editors, Proceedings of the SPIE Medical Imaging Conference 2013, 866910; doi:10.1117/12.2006783, SPIE, Bellingham, WA, 2013.
[85] 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.
[Nominated for Best Paper Award]
[86] D. Thierens and P.A.N. Bosman. Evolvability Analysis of the Linkage Tree Genetic Algorithm. In C.A. Coello Coello et al., editors, Parallel Problem Solving from Nature - PPSN XII, pages 286-295, Springer-Verlag, Berlin, 2012.
[87] N.H. Luong and P.A.N. Bosman. Elitist Archiving for Multi-Objective Evolutionary Algorithms: To Adapt or Not To Adapt. In C.A. Coello Coello et al., editors, Parallel Problem Solving from Nature - PPSN XII, pages 72-81, Springer-Verlag, Berlin, 2012.
[88] P.A.N. Bosman and T. Alderliesten. Incremental Gaussian Model-Building in Multi-Objective EDAs with an Application to Deformable Image Registration. In T. Soule and J.H. Moore, editors, Proceedings of the Genetic and Evolutionary Computation Conference - GECCO-2012, pages 241-248, ACM Press, New York, New York, 2012.
[89] P.A.N. Bosman and D. Thierens. Linkage Neighbors, Optimal Mixing and Forced Improvements in Genetic Algorithms. In T. Soule and J.H. Moore, editors, Proceedings of the Genetic and Evolutionary Computation Conference - GECCO-2012, pages 585-592, ACM Press, New York, New York, 2012.
[90] D. Thierens and P.A.N. Bosman. Predetermined versus Learned Linkage Models. In T. Soule and J.H. Moore, editors, Proceedings of the Genetic and Evolutionary Computation Conference - GECCO-2012, pages 289-296, ACM Press, New York, New York, 2012.
[91] D. Thierens and P.A.N. Bosman. Learning the Neighborhood with the Linkage Tree Genetic Algorithm. In Y. Hamadi and M. Schoenauer, editors, Proceedings of the Learning and Intelligent OptimizatioN Conference - LION-2012, pages 491-496, Springer-Verlag, Berlin, 2012.
[92] T. Alderliesten, J.-J. Sonke, and P.A.N. Bosman. Multi-objective optimization for deformable image registration: proof of concept. In D. R. Haynor and S. Ourselin, editors, Proceedings of the SPIE Medical Imaging Conference 2012, 831420; doi:10.1117/12.911268 SPIE, Bellingham, WA, 2012.
[93] S. Ramezani, P.A.N. Bosman, and H. La Poutré. Adaptive Strategies for Dynamic Pricing Agents. In O. Boissier et al., editors, Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology - IAT-2011, pages 323-328, IEEE Computer Society, Washington, DC, 2011.
[94] D. Thierens and P.A.N. Bosman. Optimal Mixing Evolutionary Algorithms. In N. Krasnogor et al., editors, Proceedings of the Genetic and Evolutionary Computation Conference - GECCO-2011, pages 617-624, ACM Press, New York, New York, 2011.
[95] 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.
[Winner of Best Paper Award]
[96] A.K. Hutzschenreuter, P.A.N. Bosman, and H. La Poutré. Enhanced Hospital Resource Management using Anticipatory Policies in Online Dynamic Multi-objective Optimization. In J. Branke et al., editors, Proceedings of the Genetic and Evolutionary Computation Conference - GECCO-2010, pages 541-542, ACM Press, New York, New York, 2010.
[97] I.B. Vermeulen, S.M. Bohte, P.A.N. Bosman, S.G. Elkhuizen, P.J.M. Bakker, and J.A. La Poutré. Optimization of Online Patient Scheduling with Urgencies and Preferences. In C. Combi, Y. Shahar, and A. Abu-Hanna, editors, Artificial Intelligence in Medicine - AIME 2009, pages 71-80, Springer-Verlag, Berlin, 2009.
[98] 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.
[Winner of Best Internationally Published Paper Award at BNAIC-2009]
[99] A.K. Hutzschenreuter, P.A.N. Bosman, and H. La Poutré. Evolutionary Multiobjective Optimization for Dynamic Hospital Resource Management. In M. Ehrgott et al., editors, Evolutionary Multi-Criterion Optimization - EMO 2009, pages 320-334, Springer-Verlag, Berlin, 2009.
[100] P.A.N. Bosman, J. Grahl, and D. Thierens. Enhancing the Performance of Maximum-Likelihood Gaussian EDAs Using Anticipated Mean Shift. In G. Rudolph et al., editors, Parallel Problem Solving from Nature - PPSN X, pages 133-143, Springer-Verlag, Berlin, 2008.
[101] A.K. Hutzschenreuter, P.A.N. Bosman, I. Blonk-Altena, J. van Aarle, and H. La Poutré. Agent-based Patient Admission Scheduling in Hospitals. In L. Padgham et al., editors, Autonomous Agents and Multiagent Systems - AAMAS-2008, pages 45-54, IFAAMAS, 2008.
[102] P.A.N. Bosman and H. La Poutré. Inventory Management and the Impact of Anticipation in Evolutionary Stochastic Online Dynamic Optimization. In Proceedings of the IEEE Congress on Evolutionary Computation - CEC-2007, pages 268-275, IEEE Press, Piscataway, New Jersey, 2007.
[103] 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.
[Nominated for Best Paper Award]
[104] J. Grahl, P.A.N. Bosman, and S. Minner. Convergence Phases, Variance Trajectories, and Runtime Analysis of Continuous EDAs. In D. Thierens et al., editors, Proceedings of the Genetic and Evolutionary Computation Conference - GECCO-2007, pages 516-522, ACM Press, New York, New York, 2007.
[105] P.A.N. Bosman and D. Thierens. Adaptive Variance Scaling in Continuous Multi-Objective Estimation-of-Distribution Algorithms. In D. Thierens et al., editors, Proceedings of the Genetic and Evolutionary Computation Conference - GECCO-2007, pages 500-507, ACM Press, New York, New York, 2007.
[106] P.A.N. Bosman, J. Grahl, and F. Rothlauf. SDR: A Better Trigger for Adaptive Variance Scaling in Normal EDAs. In D. Thierens et al., editors, Proceedings of the Genetic and Evolutionary Computation Conference - GECCO-2007, pages 492-499, ACM Press, New York, New York, 2007.
[107] P.A.N. Bosman and H. La Poutré. Computationally Intelligent Online Dynamic Vehicle Routing by Explicit Load Prediction in an Evolutionary Algorithm. In T.P. Runarsson et al., editors, Parallel Problem Solving from Nature - PPSN IX, pages 312-321, Springer-Verlag, Berlin, 2006.
[108] 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.
[Nominated for Best Paper Award]
[109] J. Grahl, P.A.N. Bosman, and F. Rothlauf. The Correlation-Triggered Adaptive Variance Scaling IDEA. In M. Keijzer et al., editors, Proceedings of the Genetic and Evolutionary Computation Conference - GECCO-2006, pages 397-404, ACM Press, New York, New York, 2006.
[110] P.A.N. Bosman and E.D. de Jong. Exploiting Gradient Information in Numerical Multi-Objective Evolutionary Optimization. In H.-G. Beyer et al., editors, Proceedings of the Genetic and Evolutionary Computation Conference - GECCO-2005, pages 755-762, ACM Press, New York, New York, 2005.
[111] P.A.N. Bosman and D. Thierens. The Naive MIDEA: a Baseline Multi-Objective EA. In C.A. Coello Coello, A.H. Aguirre, and E. Zitzler, editors, Evolutionary Multi-Criterion Optimization - EMO 2005, pages 428-442, Springer-Verlag, Berlin, 2005.
[112] P.A.N. Bosman and E.D. de Jong. Learning Probabilistic Tree Grammars for Genetic Programming. In X. Yao et al., editors, Parallel Problem Solving from Nature - PPSN VIII, pages 192-201, Springer-Verlag, Berlin, 2004.
[113] P.A.N. Bosman and D. Thierens. Permutation Optimization by Iterated Estimation of Random Keys Marginal Product Factorizations. In J.J. Merelo et al., editors, Parallel Problem Solving From Nature - PPSN VII, pages 331-340, Springer-Verlag, Berlin, 2002.
[114] P.A.N. Bosman and D. Thierens. Crossing the Road to Efficient IDEAs for Permutation Problems. In L. Spector et al., editors, Proceedings of the Genetic and Evolutionary Computation Conference - GECCO-2001, pages 219-226, Morgan Kaufmann Publishers, 2001.
[115] D. Thierens and P.A.N. Bosman Multi-Objective Mixture-based Iterated Density Estimation Evolutionary Algorithms. In L. Spector et al., editors, Proceedings of the Genetic and Evolutionary Computation Conference - GECCO-2001, pages 663-670, Morgan Kaufmann Publishers, 2001.
[116] D. Thierens and P.A.N. Bosman. Multi-objective Optimization with Iterated Density Estimation Evolutionary Algorithms Using Mixture Models. In A. Ochoa et al., editors, Proceedings of the International Symposium on Adaptive Systems 2001 - Evolutionary Computation and Probabilistic Graphical Models, pages 129-136, 2001.
[117] P.A.N. Bosman and D. Thierens. Expanding From Discrete To Continuous Estimation Of Distribution Algorithms: The IDEA. In M. Schoenauer et al., editors, Parallel Problem Solving From Nature - PPSN VI, pages 767-776, Springer-Verlag, 2000.
[118] P.A.N. Bosman and D. Thierens. Linkage Information Processing In Distribution Estimation Algorithms. In W. Banzhaf et al., editors, Proceedings of the Genetic and Evolutionary Computation Conference - GECCO-1999, pages 60-67, Morgan Kaufmann Publishers, 1999.

Chapters in books
[119] R. van der Laarse and P.A.N. Bosman. Dose Optimization. In D.Y. Song, B.R. Pieters, and K. Tanderup, editors, Emerging Technologies in Brachytherapy, pages 79-98, CRC Press, Boca Raton, Florida, 2017.
[120] P.A.N. Bosman and H. La Poutré. Online Transportation and Logistics using Computationally Intelligent Anticipation. In A. Fink and F. Rothlauf, editors, Advances in Computational Intelligence in Transport, Logistics, and Supply Chain Management, pages 185-208, Springer-Verlag, Berlin, 2008.
[121] P.A.N. Bosman and E.D. de Jong. Adaptation of a Success Story in GAs: Estimation-of-Distribution Algorithms for Tree-based Optimization Problems. In A. Yang and Y. Shan, editors, Success in Evolutionary Computation, pages 3-18, Springer-Verlag, Berlin, 2008.
[122] J. Grahl, S. Minner, and P.A.N. Bosman. Learning Structure Illuminates Black Boxes: an Introduction into Estimation of Distribution Algorithms. In Z. Michalewicz and P. Siarry, editors, Advances in Metaheuristics for Hard Optimization, pages 365-396, Springer-Verlag, Berlin, 2008.
[123] P.A.N. Bosman. Learning and Anticipation in Online Dynamic Optimization. In S. Yang, Y.S. Ong, and Y. Jin, editors, Evolutionary Computation in Dynamic and Uncertain Environments, pages 129-152, Springer-Verlag, Berlin, 2007.
[124] P.A.N. Bosman and D. Thierens. Numerical Optimization with Real-Valued Estimation-of-Distribution Algorithms. In M. Pelikan, K. Sastry, and E. Cantú-Paz, editors, Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications, pages 91-120, Springer-Verlag, Berlin, 2006.
[125] P.A.N. Bosman and D. Thierens. Multi-objective optimization with the naive MIDEA. In J.A. Lozano et al., editors, Towards a New Evolutionary Computation. Advances in Estimation of Distribution Algorithms, pages 123-157, Springer-Verlag, Berlin, 2006.
[126] P.A.N. Bosman and D. Thierens. Learning Probabilistic Models for Enhanced Evolutionary Computation. In Y. Jin, editor, Knowledge Incorporation in Evolutionary Computation, pages 147-176, Springer-Verlag, Berlin, 2004.

Papers in international workshop proceedings
[127] A. Bouter, K. Pirpinia, T. Alderliesten, and P.A.N. Bosman, Spatial Redistribution of Irregularly-Spaced Pareto Fronts for More Intuitive Navigation and Solution Selection. In D. Walker et al., organisers, Proceedings of the Visualisation in Genetic and Evolutionary Computation VizGEC Workshop at the Genetic and Evolutionary Computation Conference - GECCO-2017, pages 1697-1704, ACM Press, New York, New York, 2017.
[128] N.H. Luong, A. Bouter, M.C. van der Meer, Y. Niatsetski, C. Witteveen, A. Bel, T. Alderliesten, and P.A.N. Bosman, Efficient, Effective, and Insightful Tackling of the High-Dose-Rate Brachytherapy Treatment Planning Problem for Prostate Cancer using Evolutionary Multi-Objective Optimization Algorithms. In S.L. Smith, S. Cagnoni, and R.M. Patton, organisers, Proceedings of the Medical Applications of Genetic and Evolutionary Computation MedGEC Workshop at the Genetic and Evolutionary Computation Conference - GECCO-2017, pages 1372-1379, ACM Press, New York, New York, 2017.
[129] N.H. Luong, M.O.W. Grond, H. La Poutré, and P.A.N. Bosman, Efficiency Enhancements for Evolutionary Capacity Planning in Distribution Grids. In P.A.N. Bosman, A.-A. Tantar, and E. Tantar, organisers, Proceedings of the Green and Efficient Energy Applications of Genetic and Evolutionary Computation GreenGEC Workshop at the Genetic and Evolutionary Computation Conference - GECCO-2014, pages 1189-1196, ACM Press, New York, New York, 2014.
[130] P.A.N. Bosman and D. Thierens. The Roles of Local Search, Model Building and Optimal Mixing in Evolutionary Algorithms from a BBO Perspective. In M. Hauschild and M. Pelikan, organisers, Proceedings of the Optimization by Building and Using Probabilistic Models OBUPM Workshop at the Genetic and Evolutionary Computation Conference - GECCO-2011, pages 663-670, ACM Press, New York, New York, 2011.
[131] P.A.N. Bosman, J. Grahl, and D. Thierens. AMaLGaM IDEAs in Noisy Black-Box Optimization Benchmarking. In A. Auger et al., organisers, Proceedings of the Black Box Optimization Benchmarking BBOB Workshop at the Genetic and Evolutionary Computation Conference - GECCO-2009, pages 2351-2358, ACM Press, New York, New York, 2009.
[132] P.A.N. Bosman, J. Grahl and D. Thierens. AMaLGaM IDEAs in Noiseless Black-Box Optimization Benchmarking. In A. Auger et al., organisers, Proceedings of the Black Box Optimization Benchmarking BBOB Workshop at the Genetic and Evolutionary Computation Conference - GECCO-2009, pages 2247-2254, ACM Press, New York, New York, 2009.
[133] P.A.N. Bosman. Learning, Anticipation and Time-Deception in Evolutionary Online Dynamic Optimization. In S. Yang and J. Branke, organisers, Proceedings of the Evolutionary Algorithms for Dynamic Optimization Problems EvoDOP Workshop at the Genetic and Evolutionary Computation Conference - GECCO-2005, pages 39-47, ACM Press, New York, New York, 2005.
[134] P.A.N. Bosman and T. Alderliesten. Evolutionary Algorithms for Medical Simulations - A Case Study in Minimally-Invasive Vascular Interventions. In S.L. Smith and S. Cagnoni, organisers, Proceedings of the Medical Applications of Genetic and Evolutionary Computation MedGEC Workshop at the Genetic and Evolutionary Computation Conference - GECCO-2005, pages 125-132, ACM Press, New York, New York, 2005.
[135] P.A.N. Bosman and E.D. de Jong. Grammar Transformations in an EDA for Genetic Programming. In M. Pelikan, K. Sastry and D. Thierens, organisers, Proceedings of the Optimization by Building and Using Probabilistic Models OBUPM Workshop at the Genetic and Evolutionary Computation Conference - GECCO-2004, pages 3-14, 2004.
[136] P.A.N. Bosman and D. Thierens. Advancing Continuous IDEAs with Mixture Distributions and Factorization Selection Metrics. In M. Pelikan and K. Sastry, organisers, Proceedings of the Optimization by Building and Using Probabilistic Models OBUPM Workshop at the Genetic and Evolutionary Computation Conference - GECCO-2001, pages 208-212, 2001.
[137] P.A.N. Bosman and D. Thierens. Continuous Iterated Density Estimation Evolutionary Algorithms Within The IDEA Framework. In M. Pelikan, H. Mühlenbein and A.O. Rodriguez, organisers, Proceedings of the Optimization by Building and Using Probabilistic Models OBUPM Workshop at the Genetic and Evolutionary Computation Conference - GECCO-2000, pages 197-200, 2000.

Papers in domestic conference proceedings
[138] P.A.N. Bosman and T. Alderliesten. Bringing IDEAs into Practice: Optimization in a Minimally Invasive Vascular Intervention Simulation System. In R. Verbrugge, N. Taatgen, and L. Schomaker, editors, Proceedings of the Belgium-Netherlands Conference on Artificial Intelligence - BNAIC-2004, pages 115-122, 2004.
[139] P.A.N. Bosman and D. Thierens. Exploiting Gradient Information in Continuous Iterated Density Estimation Evolutionary Algorithms. In B. Kröse et al., editors, Proceedings of the Belgium-Netherlands Conference on Artificial Intelligence - BNAIC-2001, pages 69-76, 2001.
[140] P.A.N. Bosman and D. Thierens. Negative Log-Likelihood And Statistical Hypothesis Testing As The Basis Of Model Selection In IDEAs. In A. Feelders, editor, Proceedings of the Belgium-Netherlands Conference on Machine Learning - BENELEARN-2000, pages 109-116, 2000.
[141] P.A.N. Bosman and D. Thierens. On The Modelling Of Evolutionary Algorithms. In E. Postma and M. Gyssens, editors, Proceedings of the Belgium-Netherlands Conference on Artificial Intelligence - BNAIC-1999, pages 67-74, 1999.

Papers in international abstract-conference proceedings
[142] N.H. Luong, T. Alderliesten, A. Bel, Y. Niatsetski, and P.A.N. Bosman. Fast and insightful bi-objective HDR prostate brachytherapy planning. At The European SocieTy for Radiotherapy & Oncology conference - ESTRO-2018. 2018.
[143] 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. At The European SocieTy for Radiotherapy & Oncology conference - ESTRO-2018. 2018.
[144] 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. At The European SocieTy for Radiotherapy & Oncology conference - ESTRO-2018. 2018.
[145] 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. At The European SocieTy for Radiotherapy & Oncology conference - ESTRO-2017, 2017.
[146] M. Virgolin, I.W.E.M. van Dijk, J. Wiersma, C.M. Ronckers, C. Witteveen, C.R.N. Rasch, A. Bel, T. Alderliesten, and P.A.N. Bosman. Learning to Associate Distances with Historical Patient Data to Enable Fine-grained Studying of Late Adverse Effects of Paediatric Radiotherapy: Data, Methodology, and First Results. At The International Conference on the use of Computers in Radiation Therapy - ICCR-2016, 2016.
[147] Z. Wang, I.W.E.M. van Dijk, J. Wiersma, C.M. Ronckers, F. Oldenburger, C.R.N. Rasch, P.A.N. Bosman, A. Bel, and T. Alderliesten. Assessing the Variation in 3D Dose Reconstruction based on CT Scans of Pediatric Cancer Patients Matched on Gender and Age. At The International Conference on the use of Computers in Radiation Therapy - ICCR-2016, 2016.
[148] 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. At The International Conference on the use of Computers in Radiation Therapy - ICCR-2016, 2016.

Articles in (popular) magazines (unrefereed)
[149] F. Claessen, N. Höning, B. Liefers, H. La Poutré, and P.A.N. Bosman. Market Garden: A Scalable Research Environment for Heterogeneous Electricity Markets. In ERCIM News 92, pages 25-26, 2013.
[150] A.K. Hutzschenreuter, P.A.N. Bosman, H. La Poutré. A Computational Approach to Patient Flow Logistics in Hospitals. In ERCIM News 81, pages 44-45, 2010.

Papers in international conference proceedings (unrefereed)
[151] P.A.N. Bosman and D. Thierens. New IDEAs and more ICE by Learning and Using Unconditional Permutation Factorizations. In Late-Breaking Papers of the Genetic and Evolutionary Computation Conference - GECCO-2001, pages 16-23, 2001.

Technical reports (unrefereed)
[152] P.A.N. Bosman, J. Grahl, and D. Thierens. Adapted Maximum-Likelihood Gaussian Models for Numerical Optimization with Continuous EDAs. CWI technical report SEN-E0704, ISSN 1386-369X, 2007.
[153] P.A.N. Bosman and D. Thierens. Random Keys on ICE: Marginal Product Factorized Probability Distributions in Permutation Optimization. Utrecht University technical report UU-CS-2002-54, 2002.
[154] P.A.N. Bosman and D. Thierens. A Thorough Documentation of Obtained Results on Real-Valued Continuous and Combinatorial Multi-Objective Optimization Problems Using Diversity Preserving Mixture-based Iterated Density Estimation Evolutionary Algorithms. Utrecht University technical report UU-CS-2002-52, 2002.
[155] P.A.N. Bosman and D. Thierens. Mixed IDEAs. Utrecht University technical report UU-CS-2000-45, 2000.
[156] P.A.N. Bosman and D. Thierens. IDEAs Based On The Normal Kernels Probability Density Function. Utrecht University technical report UU-CS-2000-11, 2000.
[157] P.A.N. Bosman and D. Thierens. An Algorithmic Framework For Density Estimation Based Evolutionary Algorithms. Utrecht University technical report UU-CS-1999-46, 1999.
[158] P.A.N. Bosman. EA Visualizer Tutorial. Utrecht University technical report UU-CS-1999-20, 1999.

Ph.D. thesis
[159] P.A.N. Bosman. Design and Application of Iterated Density-Estimation Evolutionary Algorithms. Ph.D. thesis, Universiteit Utrecht, Utrecht, The Netherlands, 2003.

M.Sc. Thesis
[160] 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.