Prof.dr. Bosman’s fundamental research focus is on the design and application of Evolutionary Algorithms (EAs) for single- and multi-objective optimization, and Machine Learning (ML). The optimization problems considered are typically complex to an extent where a black-box optimization (BBO), or at least a grey-box optimization (GBO), perspective is required, i.e., virtually no information (BBO) or limited information (GBO) is available (or properly understood) about the problem at hand. The designed EAs are moreover mostly model-based, meaning that 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 e.g., the BBO case), be learned online, i.e., during optimization, using techniques from fields such as ML. For problems where efficient (problem-specific) heuristics (i.e., local search (LS) techniques) are available, model-based EAs are furthermore a very solid basis for hybridization to obtain the best of both worlds in terms of efficiency and effectiveness, resulting in state-of-the-art optimization algorithms for specific problems.
Prof.dr. Bosman’s applied research focus is on the use of (model-based) EAs to solve real-world problems that require optimization and/or machine learning, which are often multi-objective in nature, together with industry- and societal partners. The primary domain of attention is the Life Sciences and Health (LSH) domain with a specific focus is on radiation oncology, including automated treatment planning, deformable image registration and 3D dose reconstruction. Other application areas include(d) smart energy systems, revenue management, transportation logistics, and patient-flow logistics.
Prof.dr. Bosman has (co-)authored over 150 peer-reviewed publications, out of which 5 received best paper awards and 10 more were nominated for a best paper award. According to Google Scholar, his h-index is 35 with a total of 4077 citations to his works (as measured on June 24, 2021). Various other awards include a silver Humies award in 2019 for obtaining real-world human-competitive awards with EAs (in the medical domain). He is an officer, executive board, and business committee member of SIGEVO, the ACM special interest group on Genetic and Evolutionary Computation, as well as program committee member of various major conferences and journals in the EA field and related fields. In 2017, Prof.dr. Bosman was the General Chair of the main conference in the field of EAs - the Genetic and Evolutionary Computation Conference (GECCO). He has furthermore organized various workshops and tutorials on various EA related topics and has been (co-)track chair and (co-)local chair at GECCO.
Finally, the (co-)acquired research grant funding by Prof.dr. Bosman totals over €8M, which includes funding from the Dutch research council, the Dutch cancer society, the Dutch children cancer-free foundation, and the European Innovation Council. Together, the associated projects fund(ed) various scientific research positions (including 24 Ph.D. student positions and various postdoc, radiation therapy technologist, and scientific programmer positions), and various high-performance computing hardware.
NWO-TTW Open Technology Programme, "DAEDALUS – Decentralized and Automated Evolutionary Deep Architecture Learning with Unprecedented Scalability" (2020)
(co-applicant) Horizon 2020 FET Proactive, "TRUST-AI – Transparent, Reliable and Unbiased Smart Tool for AI" (2020)
NWO Open Competition Domain Science – KLEIN-2 programme, "EXAMINE – Evolutionary eXplainable Artificial Medical INtelligence Engine" (2019)
(co-applicant) Dutch Cancer Society (KWF), "Fast, accurate, and insightful brachytherapy treatment planning for cervical cancer through artificial intelligence" (2019)
NWO-ENW Joint eScience and Data Science across the Topsectors Programme, "FEDMix: Fusible Evolutionary Deep Neural Network Mixture Learning from Distributed Data for Robust Medical Image Analysis" (2017)
ﾠNVIDIA GPU Grant Programme,ﾠ"Support for the GPU-based Acceleration of Gene-pool Optimal Mixing Evolutionary Algorithms" (2017)
(co-applicant) NWO-TTW Open Technology Programme, "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" (2017)
(co-applicant) Nijbakker-Morra Stichting, "High Performance Computing System for Research into Mapping Out more Accurately than Ever Before the Irradiation-Induced Long-Term Effects after Surviving Childhood Cancer." (2017)
STW-KWF Partnership Programme,ﾠﾠ"Improving Childhood Cancer Care when Parents Cannot be There - Reducing Medical Traumatic Stress in Childhood Cancer Patients by Bonding with a Robot Companion" (2016)
(co-applicant) Maurits en Anna de Kock Stichting, "High Performance Computing System for the Accurate Reconstruction of the 3D Dose Distribution for Children with Cancer who have been Treated in the Past." (2016)
NWO Innovatieve PPS in ICT (IPPSI) - Technology Area (TA) programme,ﾠﾠ"ICT-based Innovations in the Battle against Cancer - Next-Generation Patient-Tailored Brachytherapy Cancer Treatment Planning",ﾠﾠ2015. (2015)
(co-applicant) KiKa multiannual research projects, "3D dose reconstruction for children with long-term follow-up - Toward improved decision making in radiation treatment for children with cancer." (2014)
EIT ICT Labs, "Market-driven Simulation Software for Smart Energy Systems" (2013)
EIT ICT Labs, "Market-driven Simulation Software for Smart Energy Systems" (2012)
(co-applicant) NWO free competition programme, "Estimation of Distribution Algorithms for Mixed Continuous-Discrete Problems." (2011)
EIT ICT Labs, "Market and organisational mechanisms and intelligent planning methods for smart energy systems." (2011)
(co-applicant) NWO Smart Energy Systems programme, "Computational Capacity Planning in Electricity Networks" (2010)
Current projects with external funding
Fast, accurate, and insightful brachytherapy treatment planning for cervical cancer through
artificial intelligence (Brachytherapy treatment)
Distributed and Automated Evolutionary Deep Architecture Learning with Unprecedented Scalability (DAEDALUS)
Evolutionary eXplainable Artificial Medical INtelligence Engine (EXAMINE)
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 (MODIR)
Uitlegbare kunstmatige intelligentie (None)
Transparent, Reliable and Unbiased Smart Tool for AI (TRUST-AI)