P. Bosman

P. Bosman, T. Alderliesten, M. V. Vrancken Peeters, 2021, The quarterly journal of nuclear medicine and molecular imaging : official publication of the Italian Association of Nuclear Medicine (AIMN) [and] the International Association of Radiopharmacology (IAR), [and] Section of the Society of....

Parameterless Gene-pool Optimal Mixing Evolutionary Algorithms pdf

Dirk Thierens, Peter A. N. Bosman, Marco Virgolin, 2021, ArXiv.

Random Keys on ICE: Marginal Product Factorized Probability Distributions in Permutation Optimization

Dirk Thierens, Peter A. N. Bosman, P. Bosman, 2002 .

Multi – objective optimization with the naive M ID E A

Dirk Thierens, Peter A. N. Bosman, P. Bosman, 2022 .

Evolutionary neural cascade search across supernetworks

P. Bosman, T. Alderliesten, A. Chebykin, 2022, GECCO.

Coefficient mutation in the gene-pool optimal mixing evolutionary algorithm for symbolic regression

P. Bosman, M. Virgolin, 2022, GECCO Companion.

On the impact of linkage learning, gene-pool optimal mixing, and non-redundant encoding on permutation optimization

N. H. Luong, P. Bosman, M. D. Weerdt, 2022, Swarm Evol. Comput..

Proceedings of the Genetic and Evolutionary Computation Conference - GECCO - 2006

Peter A. N. Bosman, not Cwi, M. Keijzer, 2006 .

Obtaining Smoothly Navigable Approximation Sets in Bi-Objective Multi-Modal Optimization

P. Bosman, T. Alderliesten, A. Bouter, 2022, PPSN.

Interactive Education on Sleep Hygiene with a Social Robot at a Pediatric Oncology Outpatient Clinic: Feasibility, Experiences, and Preliminary Effectiveness

P. Bosman, K. Hindriks, T. Alderliesten, 2022, Cancers.

PO-1020: Better plans and easy plan selection via bi-objective optimization for HDR prostate brachytherapy

N. H. Luong, P. Bosman, T. Alderliesten, 2018 .

EP-2236: Sensitivity of dose-volume indices to organ reconstruction settings in HDR prostate brachytherapy

P. Bosman, T. Alderliesten, A. Bel, 2018 .

PV-0188: Improved class solutions for prostate brachytherapy planning via evolutionary machine learning

P. Bosman, T. Alderliesten, A. Bel, 2017 .

Surrogate-free machine learning-based organ dose reconstruction for pediatric abdominal radiotherapy

P. Bosman, T. Alderliesten, C. Rasch, 2020, Physics in medicine and biology.

The Balance between Proximity and Diversity in Multi(cid:150)Objective Evolutionary Algorithms

P. Bosman, D. Thierens, 2022 .

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

P. Bosman, M. Gallagher, 2016, Soft Computing.

A General Framewor kA nd 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

Peter A. N. Bosman, P. Bosman, 1998 .

Delft University of Technology Machine learning for the prediction of pseudorealistic pediatric abdominal phantoms for radiation dose reconstruction

Marco Virgolin, Tanja Alderliesten, Ziyuan Wang, 2020 .

Adults as Augmentations for Children in Facial Emotion Recognition with Contrastive Learning

P. Bosman, T. Alderliesten, A. D. Lorenzo, 2022, ArXiv.

Learning Structure Illuminates Black Boxes - An Introduction to Estimation of Distribution Algorithms

Peter A. N. Bosman, Stefan Minner, Jörn Grahl, 2008, Advances in Metaheuristics for Hard Optimization.

Adaptive variance scaling in continuous multi-objective estimation-of-distribution algorithms

Dirk Thierens, Peter A. N. Bosman, P. Bosman, 2007, GECCO '07.

SDR: a better trigger for adaptive variance scaling in normal EDAs

Franz Rothlauf, Peter A. N. Bosman, Jörn Grahl, 2007, GECCO '07.

Multi-objective Optimization with the Naive MIDEA

Dirk Thierens, Peter A. N. Bosman, P. Bosman, 2006 .

Multi-objective Optimization with the Naive $$ \mathbb{M} $$ ID $$ \mathbb{E} $$ A

Dirk Thierens, Peter A. N. Bosman, P. Bosman, 2006, Towards a New Evolutionary Computation.

Numerical Optimization with Real-Valued Estimation-of-Distribution Algorithms

Dirk Thierens, Peter A. N. Bosman, P. Bosman, 2006, Scalable Optimization via Probabilistic Modeling.

Learning Probabilistic Models for Enhanced Evolutionary Computation

Dirk Thierens, Peter A. N. Bosman, P. Bosman, 2005 .

Multi-objective optimization with diversity preserving mixture-based iterated density estimation evolutionary algorithms

Dirk Thierens, Peter A. N. Bosman, P. Bosman, 2002, Int. J. Approx. Reason..

GPU-accelerated parallel gene-pool optimal mixing in a gray-box optimization setting

P. Bosman, A. Bouter, 2022, GECCO.

GPU-Accelerated Bi-Objective Treatment Planning for Prostate High-Dose-Rate Brachytherapy.

Tanja Alderliesten, Arjan Bel, Anton Bouter, 2019, Medical physics.

Large-scale parallelization of partial evaluations in evolutionary algorithms for real-world problems

Peter A. N. Bosman, Cees Witteveen, Tanja Alderliesten, 2018, GECCO.

Solving multi-structured problems by introducing linkage kernels into GOMEA

P. Bosman, D. Thierens, T. Alderliesten, 2022, GECCO.

rt 1 0 / 2 0 0 6 Learning structure illuminates black boxes – an introduction into Estimation of Distribution Algorithms

Peter A. N. Bosman, Stefan Minner, Jörn Grahl, 2006 .

Wake losses optimization of offshore wind farms with moveable floating wind turbines

Peter A. N. Bosman, Pavol Bauer, Silvio Rodrigues, 2015 .

Agent-based patient admission scheduling in hospitals

Peter A. N. Bosman, Han La Poutré, Anke K. Hutzschenreuter, 2008, AAMAS.

On the feasibility of automatically selecting similar patients in highly individualized radiotherapy dose reconstruction for historic data of pediatric cancer survivors

Cees Witteveen, Marco Virgolin, Tanja Alderliesten, 2018, Medical physics.

Multi-objective optimization of wind farm layouts – Complexity, constraint handling and scalability

Peter A. N. Bosman, Pavol Bauer, Silvio Rodrigues, 2016 .

A novel population-based multi-objective CMA-ES and the impact of different constraint handling techniques

Peter A. N. Bosman, Pavol Bauer, Sílvio Miguel Fragoso Rodrigues, 2014, GECCO.

Towards artificial intelligence-based automated treatment planning in clinical practice: A prospective study of the first clinical experiences in high-dose-rate prostate brachytherapy.

P. Bosman, T. Alderliesten, A. Bel, 2023, Brachytherapy.

Convolutional neural network surrogate-assisted GOMEA

Peter A. N. Bosman, Tanja Alderliesten, Adrienne Mendrik, 2019, GECCO.

Adaptive objective configuration in bi-objective evolutionary optimization for cervical cancer brachytherapy treatment planning

P. Bosman, T. Alderliesten, E. Kerkhof, 2022, GECCO.

DISTRIBUTION SYSTEM

N. C. Koutsoukis, N. H. Luong, P. Bosman, 2020, Applications of Modern Heuristic Optimization Methods in Power and Energy Systems.

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

Peter A. N. Bosman, Marcus Gallagher, P. Bosman, 2018, Soft Comput..

Uncrowded Hypervolume-Based Multiobjective Optimization with Gene-Pool Optimal Mixing pdf

S. C. Maree, T. Alderliesten, P. A. N. Bosman, 2020, Evolutionary Computation.

The balance between proximity and diversity in multiobjective evolutionary algorithms

Dirk Thierens, Peter A. N. Bosman, P. Bosman, 2003, IEEE Trans. Evol. Comput..

Multi-Objective Learning to Predict Pareto Fronts Using Hypervolume Maximization pdf

Monika Grewal, Tanja Alderliesten, Timo M. Deist, 2021, ArXiv.

Multi-objective Optimization by Uncrowded Hypervolume Gradient Ascent

P. Bosman, T. Deist, T. Alderliesten, 2020, PPSN.

Evolutionary Machine Learning for Multi-Objective Class Solutions in Medical Deformable Image Registration

Peter A. N. Bosman, Tanja Alderliesten, Marcel van Herk, 2019, Algorithms.

Elitist Archiving for Multi-Objective Evolutionary Algorithms: To Adapt or Not to Adapt

Peter A. N. Bosman, Hoang N. Luong, P. Bosman, 2012, PPSN.

Ensuring smoothly navigable approximation sets by Bezier curve parameterizations in evolutionary bi-objective optimization - applied to brachytherapy treatment planning for prostate cancer

S. C. Maree, T. Alderliesten, P. A. N. Bosman, 2020, PPSN.

Scalable genetic programming by gene-pool optimal mixing and input-space entropy-based building-block learning

Peter A. N. Bosman, Cees Witteveen, Marco Virgolin, 2017, GECCO.

On the usefulness of linkage processing for solving MAX-SAT

Dirk Thierens, Peter A. N. Bosman, Krzysztof L. Sadowski, 2013, GECCO '13.

Diversifying Multi-Objective Gradient Techniques and their Role in Hybrid Multi-Objective Evolutionary Algorithms for Deformable Medical Image Registration

Peter A. N. Bosman, Jan-Jakob Sonke, Tanja Alderliesten, 2015, GECCO.

A Multi-Objective Optimization Framework for Offshore Wind Farm Layouts and Electric Infrastructures

Maryam Soleimanzadeh, Peter A. N. Bosman, Pavol Bauer, 2016 .

OF SPECIALISTS IN ELECTRIC OPERATIONAL AND EXPANSION PLANNING Collection System Cable Routing and Wake Losses Optimization in Offshore Wind Farms

Delft, P. Bosman, P. Bauer, 2015 .

Real-valued evolutionary multi-modal multi-objective optimization by hill-valley clustering

Peter A. N. Bosman, Tanja Alderliesten, S. C. Maree, 2019, GECCO.

Linear scaling with and within semantic backpropagation-based genetic programming for symbolic regression

Peter A. N. Bosman, Marco Virgolin, Tanja Alderliesten, 2019, GECCO.

Fast and insightful bi-objective optimization for prostate cancer treatment planning with high-dose-rate brachytherapy

Tanja Alderliesten, Ngoc Hoang Luong, Peter A. N. Bosman, 2019, Appl. Soft Comput..

Learning the Neighborhood with the Linkage Tree Genetic Algorithm

Dirk Thierens, Peter A. N. Bosman, P. Bosman, 2012, LION.

Bi-objective optimization of organ properties for the simulation of intracavitary brachytherapy applicator placement in cervical cancer

P. Bosman, T. Alderliesten, S. M. D. Boer, 2023, Medical Imaging 2023: Image-Guided Procedures, Robotic Interventions, and Modeling.

A Computational Approach to Patient Flow Logistics in Hospitals

Peter A. N. Bosman, Han La Poutré, Anke K. Hutzschenreuter, 2010, ERCIM News.

Convolutions, transformers, and their ensembles for the segmentation of organs at risk in radiation treatment of cervical cancer

P. Bosman, T. Alderliesten, Vangelis Kostoulas, 2023, Medical Imaging.

Multi-objective mixture-based iterated density estimation evolutionary algorithms

Dirk Thierens, Peter A. N. Bosman, P. Bosman, 2001 .

Inventory management and the impact of anticipation in evolutionary stochastic online dynamic optimization

Peter A. N. Bosman, Han La Poutré, P. Bosman, 2007, 2007 IEEE Congress on Evolutionary Computation.

Computationally Intelligent Online Dynamic Vehicle Routing by Explicit Load Prediction in an Evolutionary Algorithm

Peter A. N. Bosman, Han La Poutré, P. Bosman, 2006, PPSN.

Learning Probabilistic Tree Grammars for Genetic Programming

Peter A. N. Bosman, Edwin D. de Jong, P. Bosman, 2004, PPSN.

Observer variation-aware medical image segmentation by combining deep learning and surrogate-assisted genetic algorithms

Peter A. N. Bosman, Tanja Alderliesten, Arkadiy Dushatskiy, 2020, Medical Imaging: Image Processing.

Design and Application of iterated Density-Estimation Evolutionary Algorithms

Peter A. N. Bosman, P. Bosman, 2003 .

A novel model-based evolutionary algorithm for multi-objective deformable image registration with content mismatch and large deformations: benchmarking efficiency and quality

Peter A. N. Bosman, Tanja Alderliesten, Anton Bouter, 2017, Medical Imaging.

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

Peter A. N. Bosman, Tanja Alderliesten, P. Bosman, 2016, SPIE Medical Imaging.

A first step toward uncovering the truth about weight tuning in deformable image registration

Peter A. N. Bosman, Jan-Jakob Sonke, Tanja Alderliesten, 2016, SPIE Medical Imaging.

Getting the most out of additional guidance information in deformable image registration by leveraging multi-objective optimization

Peter A. N. Bosman, Tanja Alderliesten, Arjan Bel, 2015, Medical Imaging.

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

Peter A. N. Bosman, Jan-Jakob Sonke, Tanja Alderliesten, 2015, Medical Imaging.

A multi-resolution strategy for a multi-objective deformable image registration framework that accommodates large anatomical differences

Peter A. N. Bosman, Jan-Jakob Sonke, Tanja Alderliesten, 2014, Medical Imaging.

Benchmarking Parameter-Free AMaLGaM on Functions With and Without Noise

Dirk Thierens, Peter A. N. Bosman, Jörn Grahl, 2013, Evolutionary Computation.

More concise and robust linkage learning by filtering and combining linkage hierarchies

Dirk Thierens, Peter A. N. Bosman, P. Bosman, 2013, GECCO '13.

Deformable image registration by multi-objective optimization using a dual-dynamic transformation model to account for large anatomical differences

Peter A. N. Bosman, Jan-Jakob Sonke, Tanja Alderliesten, 2013, Medical Imaging.

Multi-objective optimization for deformable image registration: proof of concept

Peter A. N. Bosman, Jan-Jakob Sonke, Tanja Alderliesten, 2012, Medical Imaging.

The roles of local search, model building and optimal mixing in evolutionary algorithms from a bbo perspective

Dirk Thierens, Peter A. N. Bosman, P. Bosman, 2011, GECCO.

AMaLGaM IDEAs in noiseless black-box optimization benchmarking

Dirk Thierens, Peter A. N. Bosman, Jörn Grahl, 2009, GECCO '09.

On empirical memory design, faster selection of bayesian factorizations and parameter-free gaussian EDAs

Peter A. N. Bosman, P. Bosman, 2009, GECCO.

Matching inductive search bias and problem structure in continuous Estimation-of-Distribution Algorithms

Peter A. N. Bosman, Jörn Grahl, P. Bosman, 2008, Eur. J. Oper. Res..

Combining gradient techniques for numerical multi-objective evolutionary optimization

Peter A. N. Bosman, Edwin D. de Jong, P. Bosman, 2006, GECCO '06.

Automatic generation of three-dimensional dose reconstruction data for two-dimensional radiotherapy plans for historically treated patients

Marco Virgolin, Tanja Alderliesten, Arjan Bel, 2020, Journal of medical imaging.

Achieving Highly Scalable Evolutionary Real-Valued Optimization by Exploiting Partial Evaluations

P. Bosman, T. Alderliesten, A. Bouter, 2020, Evolutionary Computation.

Toward self-learning model-based EAs

Peter A. N. Bosman, Erik A. Meulman, P. Bosman, 2019, GECCO.

Improving Model-Based Genetic Programming for Symbolic Regression of Small Expressions

Cees Witteveen, Marco Virgolin, Tanja Alderliesten, 2019, Evolutionary Computation.

Symbolic regression and feature construction with GP-GOMEA applied to radiotherapy dose reconstruction of childhood cancer survivors

Peter A. N. Bosman, Cees Witteveen, Marco Virgolin, 2018, GECCO.

On Gradients and Hybrid Evolutionary Algorithms for Real-Valued Multiobjective Optimization

Peter A. N. Bosman, P. Bosman, 2012, IEEE Transactions on Evolutionary Computation.

Real-valued evolutionary multi-modal optimization driven by hill-valley clustering

Dirk Thierens, Peter A. N. Bosman, Tanja Alderliesten, 2018, GECCO.

Online Transportation and Logistics Using Computationally Intelligent Anticipation

Peter A. N. Bosman, Han La Poutré, P. Bosman, 2008 .

On the modelling of evolutionary algorithms

Dirk Thierens, Peter A. N. Bosman, P. Bosman, 1999 .

Linkage Information Processing In Distribution Estimation Algorithms

Dirk Thierens, Peter A. N. Bosman, P. Bosman, 1999, GECCO.

Validation and Comparison of Radiograph-Based Organ Dose Reconstruction Approaches for Wilms Tumor Radiation Treatment Plans

P. Bosman, Choonsik Lee, C. Ronckers, 2022, Advances in radiation oncology.

Machine learning for automatic construction of pediatric abdominal phantoms for radiation dose reconstruction

Marco Virgolin, Tanja Alderliesten, Peter A. N. Bosman, 2020, Medical Imaging.

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?

Marco Virgolin, Tanja Alderliesten, Arjan Bel, 2019, Journal of radiological protection : official journal of the Society for Radiological Protection.

Automatic radiotherapy plan emulation for 3D dose reconstruction to enable big data analysis for historically treated patients

Peter A. N. Bosman, Marco Virgolin, Tanja Alderliesten, 2019, Medical Imaging.

Are age and gender suitable matching criteria in organ dose reconstruction using surrogate childhood cancer patients’ CT scans?

Tanja Alderliesten, Arjan Bel, Ziyuan Wang, 2018, Medical physics.

Clinically Acceptable Segmentation of Organs at Risk in Cervical Cancer Radiation Treatment from Clinically Available Annotations

P. Bosman, T. Alderliesten, H. Westerveld, 2023, ArXiv.

The feasibility of manual parameter tuning for deformable breast MR image registration from a multi-objective optimization perspective

Jan-Jakob Sonke, Tanja Alderliesten, Marcel van Herk, 2017, Physics in medicine and biology.

On Explaining Machine Learning Models by Evolving Crucial and Compact Features pdf

Marco Virgolin, Tanja Alderliesten, Peter A.N. Bosman, 2019, Swarm Evol. Comput..

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

Peter A. N. Bosman, Cees Witteveen, Marco Virgolin, 2016 .

Local Search is a Remarkably Strong Baseline for Neural Architecture Search pdf

P. A. N. Bosman, T. Den Ottelander, A. Dushatskiy, 2020, EMO.

Adaptive Strategies for Dynamic Pricing Agents

Peter A. N. Bosman, Han La Poutré, Sara Ramezani, 2011, 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology.

A Model-based Genetic Programming Approach for Symbolic Regression of Small Expressions pdf

Peter A. N. Bosman, Cees Witteveen, Marco Virgolin, 2019, ArXiv.

Evolvability degeneration in multi-objective genetic programming for symbolic regression

P. Bosman, T. Alderliesten, M. Virgolin, 2022, GECCO.

Bi‐objective optimization of catheter positions for high‐dose‐rate prostate brachytherapy

Tanja Alderliesten, Arjan Bel, Peter A N Bosman, 2020, Medical physics.

Automatic bi-objective parameter tuning for inverse planning of high-dose-rate prostate brachytherapy

Tanja Alderliesten, Stefanus Cornelis Maree, Peter A N Bosman, 2020, Physics in medicine and biology.

Application and benchmarking of multi-objective evolutionary algorithms on high-dose-rate brachytherapy planning for prostate cancer treatment

Peter A. N. Bosman, Tanja Alderliesten, Arjan Bel, 2017, Swarm Evol. Comput..

Multi-objective Gene-pool Optimal Mixing Evolutionary Algorithm with the Interleaved Multi-start Scheme

Peter A. N. Bosman, Han La Poutré, Ngoc Hoang Luong, 2018, Swarm Evol. Comput..

Optimal mixing evolutionary algorithms

Dirk Thierens, Peter A. N. Bosman, P. Bosman, 2011, GECCO '11.

Learning, anticipation and time-deception in evolutionary online dynamic optimization

Peter A. N. Bosman, P. Bosman, 2005, GECCO '05.

Exploiting linkage information in real-valued optimization with the real-valued gene-pool optimal mixing evolutionary algorithm

Peter A. N. Bosman, Cees Witteveen, Tanja Alderliesten, 2017, GECCO.

AMaLGaM IDEAs in noisy black-box optimization benchmarking

P. Bosman, Jörn Grahl, D. Thierens, 2009, GECCO '09.

A novel surrogate-assisted evolutionary algorithm applied to partition-based ensemble learning

Peter A. N. Bosman, Tanja Alderliesten, Arkadiy Dushatskiy, 2021, GECCO.

Shrink-Perturb Improves Architecture Mixing during Population Based Training for Neural Architecture Search

P. Bosman, T. Alderliesten, A. Dushatskiy, 2023, ArXiv.

Hybrid linkage learning for permutation optimization with Gene-pool optimal mixing evolutionary algorithms

Dirk Thierens, Peter A. N. Bosman, Ngoc Hoang Luong, 2021, GECCO Companion.

Fitness-Based Linkage Learning in the Real-Valued Gene-Pool Optimal Mixing Evolutionary Algorithm

Chantal Olieman, P. Bosman, A. Bouter, 2021, IEEE Transactions on Evolutionary Computation.

Leveraging conditional linkage models in gray-box optimization with the real-valued gene-pool optimal mixing evolutionary algorithm

Tanja Alderliesten, Anton Bouter, Stefanus C. Maree, 2020, GECCO.

Expanding from Discrete Cartesian to Permutation Gene-pool Optimal Mixing Evolutionary Algorithms

Dirk Thierens, Peter A. N. Bosman, Ngoc Hoang Luong, 2016, GECCO.

Robust Evolutionary Bi-objective Optimization for Prostate Cancer Treatment with High-Dose-Rate Brachytherapy

Peter A. N. Bosman, Tanja Alderliesten, Bradley R. Pieters, 2020, PPSN.

The anticipated mean shift and cluster registration in mixture-based EDAs for multi-objective optimization

Peter A. N. Bosman, P. Bosman, 2010, GECCO '10.

Multi-objective dual simplex-mesh based deformable image registration for 3D medical images - proof of concept

P. Bosman, T. Alderliesten, George Andreadis, 2022, Medical Imaging.

Optimization of Online Patient Scheduling with Urgencies and Preferences

Peter A. N. Bosman, Sander M. Bohte, Piet J. M. Bakker, 2009, AIME.

Machine learning for automatic construction of pseudo-realistic pediatric abdominal phantoms pdf

Peter A. N. Bosman, Marco Virgolin, Tanja Alderliesten, 2019, ArXiv.

Multi-Objective Population Based Training

P. Bosman, T. Alderliesten, A. Dushatskiy, 2023, ICML.

Mini-Batching, Gradient-Clipping, First- versus Second-Order: What Works in Gradient-Based Coefficient Optimisation for Symbolic Regression?

P. Bosman, T. Alderliesten, M. Virgolin, 2023, GECCO.

On genetic programming representations and fitness functions for interpretable dimensionality reduction

P. Bosman, T. Alderliesten, M. Virgolin, 2022, GECCO.

Model-based Genetic Programming with GOMEA for Symbolic Regression of Small Expressions

Cees Witteveen, Marco Virgolin, Tanja Alderliesten, 2019 .

Unveiling evolutionary algorithm representation with DU maps

Eric Medvet, Peter A. N. Bosman, Marco Virgolin, 2018, Genetic Programming and Evolvable Machines.

Learning and anticipation in online dynamic optimization with evolutionary algorithms: the stochastic case

Peter A. N. Bosman, Han La Poutré, P. Bosman, 2007, GECCO '07.

Advancing continuous IDEAs with mixture distributions and factorization selection metrics

Dirk Thierens, Peter A. N. Bosman, P. Bosman, 2001 .

Multi-modal multi-objective model-based genetic programming to find multiple diverse high-quality models

P. Bosman, T. Alderliesten, E. Sijben, 2022, GECCO.

Permutation Optimization by Iterated Estimation of Random Keys Marginal Product Factorizations

Dirk Thierens, Peter A. N. Bosman, P. Bosman, 2002, PPSN.

Crossing the road to efficient IDEAs for permutation problems

Dirk Thierens, Peter A. N. Bosman, P. Bosman, 2001 .

Efficient, effective, and insightful tackling of the high-dose-rate brachytherapy treatment planning problem for prostate cancer using evolutionary multi-objective optimization algorithms

Peter A. N. Bosman, Cees Witteveen, Tanja Alderliesten, 2017, GECCO.

Heed the noise in performance evaluations in neural architecture search

P. Bosman, T. Alderliesten, A. Dushatskiy, 2022, GECCO Companion.

Better and faster catheter position optimization in HDR brachytherapy for prostate cancer using multi-objective real-valued GOMEA

Peter A. N. Bosman, Tanja Alderliesten, Arjan Bel, 2018, GECCO.

Delft University of Technology Machine learning for the prediction of pseudorealistic pediatric abdominal phantoms for radiation dose reconstruction

P. Bosman, T. Alderliesten, M. Virgolin, 2020 .