Peter A. N. Bosman

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

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.

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

Dirk Thierens, Peter A. N. Bosman, Tanja Alderliesten, 2017, GECCO.

Combining gradient techniques for numerical multi-objective evolutionary optimization

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

On the usefulness of linkage processing for solving MAX-SAT

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

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.

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..

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.

Toward self-learning model-based EAs

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

Model-based evolutionary algorithms

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

Multi-objective Optimization with the Naive MIDEA

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

Niching an estimation-of-distribution algorithm by hierarchical Gaussian mixture learning

Dirk Thierens, Peter A. N. Bosman, Tanja Alderliesten, 2017, GECCO.

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.

Exploiting gradient information in continuous iterated density estimation evolutionary algorithms

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

The Naive MIDEA: A Baseline Multi-objective EA

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

A Clustering-Based Model-Building EA for Optimization Problems with Binary and Real-Valued Variables

Dirk Thierens, Peter A. N. Bosman, Krzysztof L. Sadowski, 2015, GECCO.

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.

GECCO'16 Model-Based Evolutionary Algorithms (MBEA) Workshop Chairs' Welcome

Peter A. N. Bosman, John A. W. McCall, 2016, GECCO.

Predetermined versus learned linkage models

Dirk Thierens, Peter A. N. Bosman, 2012, GECCO '12.

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

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

Optimization of Online Patient Scheduling with Urgencies and Preferences

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

Model-Based Evolutionary Algorithms

Dirk Thierens, Peter A. N. Bosman, 2015, GECCO.

Collection System Cable Routing and Wake Losses Optimization in Offshore Wind Farms

Peter A. N. Bosman, Pavol Bauer, Jan Pierik, 2014 .

In Search of Optimal Linkage Trees

Dirk Thierens, Peter A. N. Bosman, Roy de Bokx, 2015, GECCO.

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 .

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

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

Model-based evolutionary algorithms

Dirk Thierens, Peter A. N. Bosman, 2014, GECCO.

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.

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

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

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

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

Evolutionary algorithms for medical simulations: a case study in minimally-invasive vascular interventions

Peter A. N. Bosman, Tanja Alderliesten, 2005, GECCO '05.

Unveiling evolutionary algorithm representation with DU maps

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

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

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

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.

The correlation-triggered adaptive variance scaling IDEA

Franz Rothlauf, Peter A. N. Bosman, Jörn Grahl, 2006, GECCO.

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.

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 .

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

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

Proceedings of the Genetic and Evolutionary Computation Conference Companion

Peter A. N. Bosman, 2018, 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..

Convergence phases, variance trajectories, and runtime analysis of continuous EDAs

Peter A. N. Bosman, Stefan Minner, Jörn Grahl, 2007, GECCO '07.

Multi-objective optimization as a novel weight-tuning strategy for deformable image registration applied to pre-operative partial-breast radiotherapy

Peter A. N. Bosman, Tanja Alderliesten, Kleopatra Pirpinia, 2016 .

An End-to-end Deep Learning Approach for Landmark Detection and Matching in Medical Images

Peter A. N. Bosman, Monika Grewal, Tanja Alderliesten, 2020, Medical Imaging: Image Processing.

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

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

Efficiency enhancements for evolutionary capacity planning in distribution grids

Peter A. N. Bosman, Han La Poutré, Ngoc Hoang Luong, 2014, 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.

Learning Probabilistic Models for Enhanced Evolutionary Computation

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

Towards a Real-Time Minimally-Invasive Vascular Intervention Simulation System

Peter A. N. Bosman, Wiro J. Niessen, Tanja Alderliesten, 2007, IEEE Transactions on Medical Imaging.

The correlation-triggered adaptive variance scaling IDEA (CT-AVS-IDEA)

Franz Rothlauf, Peter A. N. Bosman, Jörn Grahl, 2006 .

Exploiting Linkage Information and Problem-Specific Knowledge in Evolutionary Distribution Network Expansion Planning

Peter A. N. Bosman, Han La Poutré, Ngoc Hoang Luong, 2015, GECCO.

Benchmarking the Hill-Valley Evolutionary Algorithm for the GECCO 2018 Competition on Niching Methods Multimodal Optimization pdf

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

Multi-objective mixture-based iterated density estimation evolutionary algorithms

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

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.

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

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

Enhancing the Performance of Maximum-Likelihood Gaussian EDAs Using Anticipated Mean Shift

Dirk Thierens, Peter A. N. Bosman, Jörn Grahl, 2008, PPSN.

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

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

Optimal mixing evolutionary algorithms

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

Learning and Anticipation in Online Dynamic Optimization

Peter A. N. Bosman, 2007, Evolutionary Computation in Dynamic and Uncertain Environments.

Learning and exploiting mixed variable dependencies with a model-based EA

Dirk Thierens, Peter A. N. Bosman, Krzysztof L. Sadowski, 2016, 2016 IEEE Congress on Evolutionary Computation (CEC).

Evolutionary Multiobjective Optimization for Dynamic Hospital Resource Management

Peter A. N. Bosman, Han La Poutré, Anke K. Hutzschenreuter, 2009, EMO.

Evolutionary Algorithms for Online Optimization and the Importance of Anticipation

Peter A. N. Bosman, 2007 .

Expanding from Discrete to Continuous Estimation of Distribution Algorithms: The IDEA

Dirk Thierens, Peter A. N. Bosman, P. Bosman, 2000, PPSN.

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..

Market Garden: A Simulation Environment for Research and User Experience in Smart Grids

Peter A. N. Bosman, Eric J. Pauwels, Han La Poutré, 2014, PAAMS.

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..

A Computational Approach to Patient Flow Logistics in Hospitals

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

Online Transportation and Logistics Using Computationally Intelligent Anticipation

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

Linkage Information Processing In Distribution Estimation Algorithms

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

Evolvability Analysis of the Linkage Tree Genetic Algorithm

Dirk Thierens, Peter A. N. Bosman, 2012, PPSN.

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

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

Medium-Voltage Distribution Network Expansion Planning with Gene-pool Optimal Mixing Evolutionary Algorithms

Peter A. N. Bosman, Han La Poutré, Hoang N. Luong, 2013, Artificial Evolution.

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

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

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..

Combining Model-Based EAs for Mixed-Integer Problems

Dirk Thierens, Peter A. N. Bosman, Krzysztof L. Sadowski, 2014, PPSN.

Model-based evolutionary algorithms: GECCO 2017 tutorial

Dirk Thierens, Peter A. N. Bosman, 2017, GECCO.

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

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

Design and Application of iterated Density-Estimation Evolutionary Algorithms

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

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.

On Measures to Build Linkage Trees in LTGA

Dirk Thierens, Peter A. N. Bosman, 2012, PPSN.

Incremental gaussian model-building in multi-objective EDAs with an application to deformable image registration

Peter A. N. Bosman, Tanja Alderliesten, 2012, GECCO '12.

Solving satisfiability in fuzzy logics by mixing CMA-ES

Martine De Cock, Peter A. N. Bosman, Tim Brys, 2013, GECCO '13.

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.

PO-0233 A multi-protocol validation study of automated bi-objective planning for HDR prostate brachytherapy

Peter A. N. Bosman, Tanja Alderliesten, Anton Bouter, 2021 .

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.

Two-Phase Real-Valued Multimodal Optimization with the Hill-Valley Evolutionary Algorithm

Dirk Thierens, Peter A. N. Bosman, Tanja Alderliesten, 2021 .

Model-based evolutionary algorithms

Dirk Thierens, Peter A. N. Bosman, 2021, GECCO Companion.

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

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

Parameterless Gene-pool Optimal Mixing Evolutionary Algorithms pdf

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

A Novel Approach to Designing Surrogate-assisted Genetic Algorithms by Combining Efficient Learning of Walsh Coefficients and Dependencies

Peter A. N. Bosman, Tanja Alderliesten, Arkadiy Dushatskiy, 2021, ACM Trans. Evol. Learn. Optim..

Optimization of multi-objective mixed-integer problems with a model-based evolutionary algorithm in a black-box setting

Dirk Thierens, Peter A. N. Bosman, Krzysztof L. Sadowski, 2021, GECCO Companion.

Automatic Landmarks Correspondence Detection in Medical Images with an Application to Deformable Image Registration pdf

Peter A. N. Bosman, Monika Grewal, Tanja Alderliesten, 2021, ArXiv.

GPU-Accelerated Parallel Gene-pool Optimal Mixing Applied to Multi-Objective Deformable Image Registration

Peter A. N. Bosman, Tanja Alderliesten, Anton Bouter, 2021, 2021 IEEE Congress on Evolutionary Computation (CEC).

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 .

Scalable and practical multi-objective distribution network expansion planning

Peter A. N. Bosman, Ngoc Hoang Luong, Marinus O. W. Grond, 2015, 2015 IEEE Power & Energy Society General Meeting.

PV-0256: Fast and insightful bi-objective HDR prostate brachytherapy planning

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

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 .

Sensitivity of dose‐volume indices to computation settings in high‐dose‐rate prostate brachytherapy treatment plan evaluation

Peter A. N. Bosman, Tanja Alderliesten, Arjan Bel, 2019, Journal of applied clinical medical physics.