Description
Leader of the group Life Sciences and Health: Leen Stougie.
Our research group creates fundamental knowledge and applied solutions in the broad field of life sciences. We promote understanding of how biological processes work in detail. Our interdisciplinary team of computer scientists, mathematicians and theoretical biologists develops new models, theories, and decision support systems in collaboration with experimental biologists and medical experts. We are motivated by applications of our work in practice
Watch our group video to get a glimpse of our activities or click here for more information about our group structure.
News
CWI and VU develop algorithms for pharmaceutical research
In their search for new drugs, researchers increasingly use computer simulations to discover and test new molecules for pharmaceutical use. This does not only increase the chance of finding new or better drugs, but also prevents wasting resources on unsuccessful experiments.
New network model can learn to remember past events
Scientists understand how neurons, the smallest computational units of the brain, behave during tasks, but how brains learn to make efficient choices is unknown, in particular when the brain’s working memory is involved.
CWI to improve ICT for radiotherapy
CWI launches a research project with the Academic Medical Center (AMC) and the company Elekta Brachytherapy to improve the ICT that is used in radiotherapy for cancer patients. In the next years, researchers of these organizations will investigate the improvement of medical software and instruments that are used for internal radiation.
New mathematical models predict success rate of drug trials
A large part of drug trials fail when it turns out that the results for laboratory animals do not transfer to humans (1). New mathematical models developed at Centrum Wiskunde & Informatica (CWI) are able to predict the transferability of results between different organisms.
Current events
PhD defense Ngoc Hoang Luong (LSH)
- 2018-10-17T15:00:00+02:00
- 2018-10-17T16:00:00+02:00
PhD defense Ngoc Hoang Luong (LSH)
Start: 2018-10-17 15:00:00+02:00 End: 2018-10-17 16:00:00+02:00
Everyone is invited to attend the public defense of Ngoc Hoang Luong of his PhD thesis:
Design and Application of Scalable Evolutionary Algorithms in Electricity Distribution Network Expansion Planning
Promotoren: prof.dr. Han La Poutré and prof.dr. Peter Bosman
Summary of the dissertation is here.
- Email: Ngoc Hoang Luong
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iCal
LSH Seminar Professor Kalyanmoy Deb (Michigan State University)
- 2018-10-18T14:00:00+02:00
- 2018-10-18T15:00:00+02:00
LSH Seminar Professor Kalyanmoy Deb (Michigan State University)
Start: 2018-10-18 14:00:00+02:00 End: 2018-10-18 15:00:00+02:00
Speaker:
Kalyanmoy Deb, Koenig Endowed Chair Professor
Michigan State University, East Lansing, USA, kdeb@egr.msu.edu
Title:
Breaking the Billion-Variable Barrier Using Customized Evolutionary Optimization
Abstract:
Optimization methods and practices are around for more than 50 years, but they are still criticized for their "curse of dimensionality". In this talk, we shall look at a specific large-dimensional integer-valued resource allocation problem class from practice and review the performance of well-known softwares, such as IBM's CPLEX, on the problem. Thereafter, we shall present a population-based heuristic search algorithm that has the ability to recombine short-sized building blocks, despite having overlapping variable linkage, to form larger-sized building blocks. The process is eventually able to solve a billion-variable version of the problem to near-optimality in polynomial computational time, making the application one of the largest size optimization problems ever solved.
Bio-sketch of the Speaker:
Kalyanmoy Deb is Koenig Endowed Chair Professor at Department of Electrical and Computer Engineering in Michigan State University, USA. Prof. Deb's research interests are in evolutionary optimization and their application in multi-criterion optimization, modeling, and machine learning. He has worked at various universities across the world including IITs in India, University of Dortmund and Karlsruhe Institute of Technology in Germany, Aalto University in Finland, University of Skovde in Sweden, Nanyang Technological University in Singapore. He was awarded Infosys Prize, TWAS Prize in Engineering Sciences, CajAstur Mamdani Prize, Distinguished Alumni Award from IIT Kharagpur, Edgeworth-Pareto award, Bhatnagar Prize in Engineering Sciences, and Bessel Research award from Germany. He has been awarded IEEE CIS's "EC Pioneer Award". He is fellow of IEEE, ASME, and three Indian science and engineering academies. He has published over 490 research papers with Google Scholar citation of over 117,000 with h-index 110. He is in the editorial board on 18 major international journals. More information about his research contribution can be found from http://www.egr.msu.edu/~kdeb.
Space is limited so please register for this seminar.
- Web
- Email: Prof.dr. Peter A.N. Bosman
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iCal
Members
Associated Members
Publications
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Medvet, E, Virgolin, M, Castelli, M, Bosman, P.A.N, Gonçalves, I, & Tušar, T. (2018). Unveiling evolutionary algorithm representation with DU maps. Genetic Programming and Evolvable Machines - Topical Collection: Genetic Programming, Evolutionary Computation and Visualization, 1–39. doi:10.1007/s10710-018-9332-5
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Orphanou, K, Thierens, D, & Bosman, P.A.N. (2018). Learning Bayesian network structures with GOMEA. In GECCO 2018 - Proceedings of the 2018 Genetic and Evolutionary Computation Conference (pp. 1007–1014). doi:10.1145/3205455.3205502
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Bouter, A, Alderliesten, T, Bel, A, Witteveen, C, & Bosman, P.A.N. (2018). Large-scale parallelization of partial evaluations in evolutionary algorithms for real-world problems. In GECCO 2018 - Proceedings of the 2018 Genetic and Evolutionary Computation Conference (pp. 1199–1206). doi:10.1145/3205455.3205610
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Luong, N.H, Alderliesten, T, & Bosman, P.A.N. (2018). Improving the performance of MO-RV-GOMEA on problems with many objectives using Tchebycheff scalarizations. In GECCO 2018 - Proceedings of the 2018 Genetic and Evolutionary Computation Conference (pp. 705–712). doi:10.1145/3205455.3205498
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Rens, E.G. (2018, June 27). Multiscale mathematical biology of cell-extracellular matrix interactions during morphogenesis.
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Chikhi, R, & Schönhuth, A. (2018). Dualities in tree representations. In 29th Annual Symposium on Combinatorial Pattern Matching, CPM 2018. doi:10.4230/LIPIcs.CPM.2018.18
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Wang, Z, Dijk, van, I.W.E.M, Wiersma, J, Ronckers, C.M, Oldenburger, F, Balgobind, B.V, … Alderliesten, T. (2018). Are age and gender suitable matching criteria in organ dose reconstruction using surrogate childhood cancer patients' CT scans?. Medical Physics. doi:10.1002/mp.12908
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Virgolin, M, Dijk, van, I.W.E.M, Wiersma, J, Ronckers, C.M, Witteveen, C, Bel, A, … Bosman, P.A.N. (2018). On the feasibility of automatically selecting similar patients in highly individualized radiotherapy dose reconstruction for historic data of pediatric cancer survivors. Medical Physics. doi:10.1002/mp.12802
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Sadowski, K.L, Thierens, D, & Bosman, P.A.N. (2018). GAMBIT: A parameterless model-based evolutionary algorithm for mixed-integer problems. Evolutionary Computation, 26(1), 117–143. doi:10.1162/evco_a_00206
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Luong, N.H, La Poutré, J.A, & Bosman, P.A.N. (2018). Multi-objective Gene-pool Optimal Mixing Evolutionary Algorithm with the interleaved multi-start scheme. Swarm and Evolutionary Computation. doi:10.1016/j.swevo.2018.02.005
Software
VirtualLeaf: a modeling framework for plant tissue morphogenesis
VirtualLeaf is a computer modeling framework for the simulation of plant tissue morphogenesis, i.e., the biological development of an organism's shape
Current projects with external funding
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3D dose reconstruction for children with long-term follow-up Toward improved decision making in radiation treatment for children with cancer
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Enhancing protein-drug binding prediction
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ICT based Innovations in the Battle against Cancer – Next - Generation Patient -Tailored Brachytherapy Cancer Treatment Planning
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Improving Childhood Cancer Care when Parents Cannot be There - Reducing Medical Traumatic Stress in Childhood Cancer Patients by Bonding with a Robot Companion
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Statistical Models for Structural Genetic Variants in the Genome of the Netherlands
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Identificatie van homogene subgroepen bij mensen met een chronische ziekte, die ernstig vermoeid zijn, door middel van een persoonsgerichte studie opzet. (Beter Gezond)
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Fusible Evolutionary Deep Neural Network Mixture Learning from Distributed Data for Robust Medical Image Analysis (FEDMix)
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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)
Related partners
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AMC Medical Research
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Elekta Limited
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Nucletron Operations BV
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Xomnia
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Academisch Medisch Centrum
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Biomedical Imaging Group Rotterdam
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Erasmus Universiteit Rotterdam
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KiKa
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Technische Universiteit Delft
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Universiteit Leiden