default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDPeter A. N. Bosman
Person information
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2024
[c139]Georgios Andreadis
, Tanja Alderliesten, Peter A. N. Bosman:
Fitness-based Linkage Learning and Maximum-Clique Conditional Linkage Modelling for Gray-box Optimization with RV-GOMEA. GECCO 2024- [c138]Arthur Guijt, Dirk Thierens, Tanja Alderliesten, Peter A. N. Bosman:
Exploring the Search Space of Neural Network Combinations obtained with Efficient Model Stitching. GECCO Companion 2024: 1914-1923 - [c137]Cedric J. Rodriguez, Sarah L. Thomson, Tanja Alderliesten, Peter A. N. Bosman:
Temporal True and Surrogate Fitness Landscape Analysis for Expensive Bi-Objective Optimisation. GECCO 2024 - [c136]Thalea Schlender, Mafalda Malafaia, Tanja Alderliesten, Peter A. N. Bosman:
Improving the efficiency of GP-GOMEA for higher-arity operators. GECCO 2024 - [c135]Evi Sijben, Jeroen Jansen, Peter A. N. Bosman, Tanja Alderliesten:
Function Class Learning with Genetic Programming: Towards Explainable Meta Learning for Tumor Growth Functionals. GECCO 2024 - [c134]Dirk Thierens, Peter A. N. Bosman:
Model-Based Evolutionary Algorithms. GECCO Companion 2024: 1096-1126 - [c133]Johannes Koch, Tanja Alderliesten, Peter A. N. Bosman:
Simultaneous Model-Based Evolution of Constants and Expression Structure in GP-GOMEA for Symbolic Regression. PPSN (1) 2024: 238-255 - [c132]Cedric J. Rodriguez, Peter A. N. Bosman, Tanja Alderliesten:
Balancing Between Time Budgets and Costs in Surrogate-Assisted Evolutionary Algorithms. PPSN (2) 2024: 322-339 - [c131]Damy M. F. Ha, Tanja Alderliesten, Peter A. N. Bosman:
Learning Discretized Bayesian Networks with GOMEA. PPSN (3) 2024: 352-368 - [i53]Georgios Andreadis, Joas I. Mulder, Anton Bouter, Peter A. N. Bosman, Tanja Alderliesten:
A Tournament of Transformation Models: B-Spline-based vs. Mesh-based Multi-Objective Deformable Image Registration. CoRR abs/2401.16867 (2024) - [i52]Thalea Schlender, Mafalda Malafaia, Tanja Alderliesten, Peter A. N. Bosman:
Improving the efficiency of GP-GOMEA for higher-arity operators. CoRR abs/2402.09854 (2024) - [i51]Georgios Andreadis, Tanja Alderliesten, Peter A. N. Bosman:
Fitness-based Linkage Learning and Maximum-Clique Conditional Linkage Modelling for Gray-box Optimization with RV-GOMEA. CoRR abs/2402.10757 (2024) - [i50]Damy M. F. Ha, Tanja Alderliesten, Peter A. N. Bosman:
Learning Discretized Bayesian Networks with GOMEA. CoRR abs/2402.12175 (2024) - [i49]Mafalda Malafaia, Thalea Schlender, Peter A. N. Bosman, Tanja Alderliesten:
MultiFIX: An XAI-friendly feature inducing approach to building models from multimodal data. CoRR abs/2402.12183 (2024) - [i48]E. M. C. Sijben, J. C. Jansen, Peter A. N. Bosman, Tanja Alderliesten:
Function Class Learning with Genetic Programming: Towards Explainable Meta Learning for Tumor Growth Functionals. CoRR abs/2402.12510 (2024) - [i47]Monika Grewal, Henrike Westerveld, Peter A. N. Bosman, Tanja Alderliesten:
Multi-Objective Learning for Deformable Image Registration. CoRR abs/2402.16658 (2024) - [i46]Arthur Guijt, Dirk Thierens, Tanja Alderliesten, Peter A. N. Bosman:
Stitching for Neuroevolution: Recombining Deep Neural Networks without Breaking Them. CoRR abs/2403.14224 (2024) - [i45]Alexander Chebykin, Peter A. N. Bosman, Tanja Alderliesten:
Hyperparameter-Free Medical Image Synthesis for Sharing Data and Improving Site-Specific Segmentation. CoRR abs/2404.06240 (2024) - [i44]Cedric J. Rodriguez, Sarah L. Thomson, Tanja Alderliesten, Peter A. N. Bosman:
Temporal True and Surrogate Fitness Landscape Analysis for Expensive Bi-Objective Optimisation. CoRR abs/2404.06557 (2024) - [i43]E. M. C. Sijben, J. C. Jansen, M. de Ridder, Peter A. N. Bosman, Tanja Alderliesten:
Deep learning-based auto-segmentation of paraganglioma for growth monitoring. CoRR abs/2404.07952 (2024) - 2023
- [c130]Alexander Chebykin, Arkadiy Dushatskiy, Tanja Alderliesten, Peter A. N. Bosman:
Shrink-Perturb Improves Architecture Mixing During Population Based Training for Neural Architecture Search. ECAI 2023: 381-388 - [c129]Timo M. Deist, Monika Grewal, Frank J. W. M. Dankers, Tanja Alderliesten, Peter A. N. Bosman:
Multi-objective Learning Using HV Maximization. EMO 2023: 103-117 - [c128]Arthur Guijt, Dirk Thierens, Tanja Alderliesten, Peter A. N. Bosman:
The Impact of Asynchrony on Parallel Model-Based EAs. GECCO 2023: 910-918 - [c127]Dirk Thierens, Peter A. N. Bosman:
Model-Based Evolutionary Algorithms. GECCO Companion 2023: 1099-1128 - [c126]Joe Harrison, Marco Virgolin, Tanja Alderliesten, Peter A. N. Bosman:
Mini-Batching, Gradient-Clipping, First- versus Second-Order: What Works in Gradient-Based Coefficient Optimisation for Symbolic Regression? GECCO 2023: 1127-1136 - [c125]Georgios Andreadis, Peter A. N. Bosman, Tanja Alderliesten:
MOREA: a GPU-accelerated Evolutionary Algorithm for Multi-Objective Deformable Registration of 3D Medical Images. GECCO 2023: 1294-1302 - [c124]Anton Bouter, Peter A. N. Bosman:
A Joint Python/C++ Library for Efficient yet Accessible Black-Box and Gray-Box Optimization with GOMEA. GECCO Companion 2023: 1864-1872 - [c123]Arkadiy Dushatskiy, Alexander Chebykin, Tanja Alderliesten, Peter A. N. Bosman:
Multi-Objective Population Based Training. ICML 2023: 8969-8989 - [c122]Monika Grewal, Dustin van Weersel, Henrike Westerveld, Peter A. N. Bosman, Tanja Alderliesten:
Learning Clinically Acceptable Segmentation of Organs at Risk in Cervical Cancer Radiation Treatment from Clinically Available Annotations. MIDL 2023: 260-273 - [c121]Cedric J. Rodriguez, Stephanie M. de Boer, Peter A. N. Bosman, Tanja Alderliesten:
Bi-objective optimization of organ properties for the simulation of intracavitary brachytherapy applicator placement in cervical cancer. Medical Imaging: Image-Guided Procedures 2023 - [c120]Vangelis Kostoulas, Peter A. N. Bosman, Tanja Alderliesten:
Convolutions, transformers, and their ensembles for the segmentation of organs at risk in radiation treatment of cervical cancer. Medical Imaging: Image Processing 2023 - [i42]Monika Grewal, Dustin van Weersel, Henrike Westerveld, Peter A. N. Bosman, Tanja Alderliesten:
Clinically Acceptable Segmentation of Organs at Risk in Cervical Cancer Radiation Treatment from Clinically Available Annotations. CoRR abs/2302.10661 (2023) - [i41]Georgios Andreadis, Peter A. N. Bosman, Tanja Alderliesten:
MOREA: a GPU-accelerated Evolutionary Algorithm for Multi-Objective Deformable Registration of 3D Medical Images. CoRR abs/2303.04873 (2023) - [i40]Vangelis Kostoulas, Peter A. N. Bosman, Tanja Alderliesten:
Convolutions, Transformers, and their Ensembles for the Segmentation of Organs at Risk in Radiation Treatment of Cervical Cancer. CoRR abs/2303.11501 (2023) - [i39]Arthur Guijt, Dirk Thierens, Tanja Alderliesten, Peter A. N. Bosman:
The Impact of Asynchrony on Parallel Model-Based EAs. CoRR abs/2303.15543 (2023) - [i38]Anton Bouter, Peter A. N. Bosman:
A Joint Python/C++ Library for Efficient yet Accessible Black-Box and Gray-Box Optimization with GOMEA. CoRR abs/2305.06246 (2023) - [i37]Arkadiy Dushatskiy, Alexander Chebykin, Tanja Alderliesten, Peter A. N. Bosman:
Multi-Objective Population Based Training. CoRR abs/2306.01436 (2023) - [i36]Alexander Chebykin, Arkadiy Dushatskiy, Tanja Alderliesten, Peter A. N. Bosman:
Shrink-Perturb Improves Architecture Mixing during Population Based Training for Neural Architecture Search. CoRR abs/2307.15621 (2023) - [i35]Anne Auger, Peter A. N. Bosman, Pascal Kerschke, Darrell Whitley, Lennart Schäpermeier:
Challenges in Benchmarking Optimization Heuristics (Dagstuhl Seminar 23251). Dagstuhl Reports 13(6): 55-80 (2023) - 2022
- [j21]S. C. Maree, Tanja Alderliesten, Peter A. N. Bosman:
Uncrowded Hypervolume-Based Multiobjective Optimization with Gene-Pool Optimal Mixing. Evol. Comput. 30(3): 329-353 (2022) - [j20]Arthur Guijt, Ngoc Hoang Luong, Peter A. N. Bosman, Mathijs de Weerdt:
On the impact of linkage learning, gene-pool optimal mixing, and non-redundant encoding on permutation optimization. Swarm Evol. Comput. 70: 101044 (2022) - [c119]E. M. C. Sijben, Tanja Alderliesten, Peter A. N. Bosman:
Multi-modal multi-objective model-based genetic programming to find multiple diverse high-quality models. GECCO 2022: 440-448 - [c118]Thomas Uriot, Marco Virgolin, Tanja Alderliesten, Peter A. N. Bosman:
On genetic programming representations and fitness functions for interpretable dimensionality reduction. GECCO 2022: 458-466 - [c117]Anton Bouter, Peter A. N. Bosman:
GPU-accelerated parallel gene-pool optimal mixing in a gray-box optimization setting. GECCO 2022: 675-683 - [c116]Arthur Guijt, Dirk Thierens, Tanja Alderliesten, Peter A. N. Bosman:
Solving multi-structured problems by introducing linkage kernels into GOMEA. GECCO 2022: 703-711 - [c115]Dazhuang Liu, Marco Virgolin, Tanja Alderliesten, Peter A. N. Bosman:
Evolvability degeneration in multi-objective genetic programming for symbolic regression. GECCO 2022: 973-981 - [c114]Alexander Chebykin, Tanja Alderliesten, Peter A. N. Bosman:
Evolutionary neural cascade search across supernetworks. GECCO 2022: 1038-1047 - [c113]Dirk Thierens, Peter A. N. Bosman:
Model-based evolutionary algorithms: GECCO 2022 tutorial. GECCO Companion 2022: 1141-1170 - [c112]Leah R. M. Dickhoff, Ellen M. Kerkhof, Heloisa H. Deuzeman, Carien L. Creutzberg, Tanja Alderliesten, Peter A. N. Bosman:
Adaptive objective configuration in bi-objective evolutionary optimization for cervical cancer brachytherapy treatment planning. GECCO 2022: 1173-1181 - [c111]Arkadiy Dushatskiy, Tanja Alderliesten, Peter A. N. Bosman:
Heed the noise in performance evaluations in neural architecture search. GECCO Companion 2022: 2104-2112 - [c110]Marco Virgolin, Peter A. N. Bosman:
Coefficient mutation in the gene-pool optimal mixing evolutionary algorithm for symbolic regression. GECCO Companion 2022: 2289-2297 - [c109]Georgios Andreadis, Peter A. N. Bosman, Tanja Alderliesten:
Multi-objective dual simplex-mesh based deformable image registration for 3D medical images - proof of concept. Medical Imaging: Image Processing 2022 - [c108]Martijn M. A. Bosma, Arkadiy Dushatskiy, Monika Grewal, Tanja Alderliesten, Peter A. N. Bosman:
Mixed-block neural architecture search for medical image segmentation. Medical Imaging: Image Processing 2022 - [c107]Arkadiy Dushatskiy, Gerry Lowe, Peter A. N. Bosman, Tanja Alderliesten:
Data variation-aware medical image segmentation. Medical Imaging: Image Processing 2022 - [c106]Joe Harrison, Tanja Alderliesten, Peter A. N. Bosman:
Gene-pool Optimal Mixing in Cartesian Genetic Programming. PPSN (2) 2022: 19-32 - [c105]Damy M. F. Ha, Timo M. Deist, Peter A. N. Bosman:
Hybridizing Hypervolume-Based Evolutionary Algorithms and Gradient Descent by Dynamic Resource Allocation. PPSN (2) 2022: 179-192 - [c104]Renzo J. Scholman, Anton Bouter, Leah R. M. Dickhoff, Tanja Alderliesten, Peter A. N. Bosman:
Obtaining Smoothly Navigable Approximation Sets in Bi-objective Multi-modal Optimization. PPSN (2) 2022: 247-262 - [d1]Arthur Guijt, Dirk Thierens, Tanja Alderliesten, Peter A. N. Bosman:
Solving multi-structured problems by introducing linkage kernels into GOMEA - Source Code. Zenodo, 2022
- [i34]Arkadiy Dushatskiy, Tanja Alderliesten, Peter A. N. Bosman:
Heed the Noise in Performance Evaluations in Neural Architecture Search. CoRR abs/2202.02078 (2022) - [i33]Marco Virgolin, Andrea De Lorenzo, Tanja Alderliesten, Peter A. N. Bosman:
Adults as Augmentations for Children in Facial Emotion Recognition with Contrastive Learning. CoRR abs/2202.05187 (2022) - [i32]Dazhuang Liu, Marco Virgolin, Tanja Alderliesten, Peter A. N. Bosman:
Evolvability Degeneration in Multi-Objective Genetic Programming for Symbolic Regression. CoRR abs/2202.06983 (2022) - [i31]Georgios Andreadis, Peter A. N. Bosman, Tanja Alderliesten:
Multi-Objective Dual Simplex-Mesh Based Deformable Image Registration for 3D Medical Images - Proof of Concept. CoRR abs/2202.11001 (2022) - [i30]Martijn M. A. Bosma, Arkadiy Dushatskiy, Monika Grewal, Tanja Alderliesten, Peter A. N. Bosman:
Mixed-Block Neural Architecture Search for Medical Image Segmentation. CoRR abs/2202.11401 (2022) - [i29]Arkadiy Dushatskiy, Gerry Lowe, Peter A. N. Bosman, Tanja Alderliesten:
Data variation-aware medical image segmentation. CoRR abs/2202.12099 (2022) - [i28]Thomas Uriot, Marco Virgolin, Tanja Alderliesten, Peter A. N. Bosman:
On genetic programming representations and fitness functions for interpretable dimensionality reduction. CoRR abs/2203.00528 (2022) - [i27]Alexander Chebykin, Tanja Alderliesten, Peter A. N. Bosman:
Evolutionary Neural Cascade Search across Supernetworks. CoRR abs/2203.04011 (2022) - [i26]Arthur Guijt, Dirk Thierens, Tanja Alderliesten, Peter A. N. Bosman:
Solving Multi-Structured Problems by Introducing Linkage Kernels into GOMEA. CoRR abs/2203.05970 (2022) - [i25]Anton Bouter, Peter A. N. Bosman:
GPU-Accelerated Parallel Gene-pool Optimal Mixing in a Gray-Box Optimization Setting. CoRR abs/2203.08680 (2022) - [i24]Leah R. M. Dickhoff, Ellen M. Kerkhof, Heloisa H. Deuzeman, Carien L. Creutzberg, Tanja Alderliesten, Peter A. N. Bosman:
Adaptive Objective Configuration in Bi-Objective Evolutionary Optimization for Cervical Cancer Brachytherapy Treatment Planning. CoRR abs/2203.08851 (2022) - [i23]Renzo J. Scholman, Anton Bouter, Leah R. M. Dickhoff, Tanja Alderliesten, Peter A. N. Bosman:
Obtaining Smoothly Navigable Approximation Sets in Bi-Objective Multi-Modal Optimization. CoRR abs/2203.09214 (2022) - [i22]E. M. C. Sijben, Tanja Alderliesten, Peter A. N. Bosman:
Multi-modal multi-objective model-based genetic programming to find multiple diverse high-quality models. CoRR abs/2203.13347 (2022) - [i21]Marco Virgolin, Eric Medvet, Tanja Alderliesten, Peter A. N. Bosman:
Less is More: A Call to Focus on Simpler Models in Genetic Programming for Interpretable Machine Learning. CoRR abs/2204.02046 (2022) - [i20]Marco Virgolin, Peter A. N. Bosman:
Coefficient Mutation in the Gene-pool Optimal Mixing Evolutionary Algorithm for Symbolic Regression. CoRR abs/2204.12159 (2022) - 2021
- [j19]Anton Bouter, Tanja Alderliesten, Peter A. N. Bosman:
Achieving Highly Scalable Evolutionary Real-Valued Optimization by Exploiting Partial Evaluations. Evol. Comput. 29(1): 129-155 (2021) - [j18]Marco Virgolin, Tanja Alderliesten, Cees Witteveen, Peter A. N. Bosman:
Improving Model-Based Genetic Programming for Symbolic Regression of Small Expressions. Evol. Comput. 29(2): 211-237 (2021) - [j17]Chantal Olieman, Anton Bouter, Peter A. N. Bosman:
Fitness-Based Linkage Learning in the Real-Valued Gene-Pool Optimal Mixing Evolutionary Algorithm. IEEE Trans. Evol. Comput. 25(2): 358-370 (2021) - [j16]Arkadiy Dushatskiy, Tanja Alderliesten, Peter A. N. Bosman:
A Novel Approach to Designing Surrogate-assisted Genetic Algorithms by Combining Efficient Learning of Walsh Coefficients and Dependencies. ACM Trans. Evol. Learn. Optim. 1(2): 5:1-5:23 (2021) - [c103]Anton Bouter, Tanja Alderliesten, Peter A. N. Bosman:
GPU-Accelerated Parallel Gene-pool Optimal Mixing Applied to Multi-Objective Deformable Image Registration. CEC 2021: 2539-2548 - [c102]Tom Den Ottelander, Arkadiy Dushatskiy, Marco Virgolin, Peter A. N. Bosman:
Local Search is a Remarkably Strong Baseline for Neural Architecture Search. EMO 2021: 465-479 - [c101]Krzysztof L. Sadowski, Dirk Thierens, Peter A. N. Bosman:
Optimization of multi-objective mixed-integer problems with a model-based evolutionary algorithm in a black-box setting. GECCO Companion 2021: 227-228 - [c100]Dirk Thierens, Peter A. N. Bosman:
Model-based evolutionary algorithms. GECCO Companion 2021: 558-587 - [c99]Arkadiy Dushatskiy, Tanja Alderliesten, Peter A. N. Bosman:
A novel surrogate-assisted evolutionary algorithm applied to partition-based ensemble learning. GECCO 2021: 583-591 - [c98]Michal Witold Przewozniczek, Marcin M. Komarnicki, Peter A. N. Bosman, Dirk Thierens, Bartosz Frej, Ngoc Hoang Luong:
Hybrid linkage learning for permutation optimization with Gene-pool optimal mixing evolutionary algorithms. GECCO Companion 2021: 1442-1450 - [p5]Stefanus C. Maree, Dirk Thierens, Tanja Alderliesten, Peter A. N. Bosman:
Two-Phase Real-Valued Multimodal Optimization with the Hill-Valley Evolutionary Algorithm. Metaheuristics for Finding Multiple Solutions 2021: 165-189 - [i19]Timo M. Deist, Monika Grewal, Frank J. W. M. Dankers, Tanja Alderliesten, Peter A. N. Bosman:
Multi-Objective Learning to Predict Pareto Fronts Using Hypervolume Maximization. CoRR abs/2102.04523 (2021) - [i18]Arkadiy Dushatskiy, Tanja Alderliesten, Peter A. N. Bosman:
A Novel Surrogate-assisted Evolutionary Algorithm Applied to Partition-based Ensemble Learning. CoRR abs/2104.08048 (2021) - [i17]Monika Grewal, Jan Wiersma, Henrike Westerveld, Peter A. N. Bosman, Tanja Alderliesten:
Automatic Landmarks Correspondence Detection in Medical Images with an Application to Deformable Image Registration. CoRR abs/2109.02722 (2021) - [i16]Arkadiy Dushatskiy, Marco Virgolin, Anton Bouter, Dirk Thierens, Peter A. N. Bosman:
Parameterless Gene-pool Optimal Mixing Evolutionary Algorithms. CoRR abs/2109.05259 (2021) - 2020
- [j15]Marco Virgolin, Tanja Alderliesten, Peter A. N. Bosman:
On explaining machine learning models by evolving crucial and compact features. Swarm Evol. Comput. 53: 100640 (2020) - [c97]Dirk Thierens, Peter A. N. Bosman:
Model-based evolutionary algorithms: GECCO 2020 tutorial. GECCO Companion 2020: 590-619 - [c96]Anton Bouter, Stefanus C. Maree, Tanja Alderliesten, Peter A. N. Bosman:
Leveraging conditional linkage models in gray-box optimization with the real-valued gene-pool optimal mixing evolutionary algorithm. GECCO 2020: 603-611 - [c95]Arkadiy Dushatskiy, Adriënne M. Mendrik, Peter A. N. Bosman, Tanja Alderliesten:
Observer variation-aware medical image segmentation by combining deep learning and surrogate-assisted genetic algorithms. Medical Imaging: Image Processing 2020: 113131B - [c94]Monika Grewal, Timo M. Deist, Jan Wiersma, Peter A. N. Bosman, Tanja Alderliesten:
An end-to-end deep learning approach for landmark detection and matching in medical images. Medical Imaging: Image Processing 2020: 1131328 - [c93]Timo M. Deist, Stefanus C. Maree, Tanja Alderliesten, Peter A. N. Bosman:
Multi-objective Optimization by Uncrowded Hypervolume Gradient Ascent. PPSN (2) 2020: 186-200 - [c92]Stefanus C. Maree, Tanja Alderliesten, Peter A. N. Bosman:
Ensuring Smoothly Navigable Approximation Sets by Bézier Curve Parameterizations in Evolutionary Bi-objective Optimization. PPSN (2) 2020: 215-228 - [c91]Marjolein C. van der Meer, Arjan Bel, Yury Niatsetski, Tanja Alderliesten, Bradley R. Pieters, Peter A. N. Bosman:
Robust Evolutionary Bi-objective Optimization for Prostate Cancer Treatment with High-Dose-Rate Brachytherapy. PPSN (2) 2020: 441-453 - [i15]Monika Grewal, Timo M. Deist, Jan Wiersma, Peter A. N. Bosman, Tanja Alderliesten:
An End-to-end Deep Learning Approach for Landmark Detection and Matching in Medical Images. CoRR abs/2001.07434 (2020) - [i14]Arkadiy Dushatskiy, Adriënne M. Mendrik, Peter A. N. Bosman, Tanja Alderliesten:
Observer variation-aware medical image segmentation by combining deep learning and surrogate-assisted genetic algorithms. CoRR abs/2001.08552 (2020) - [i13]Marco Virgolin, Ziyuan Wang, Brian V. Balgobind, Irma W. E. M. van Dijk, Jan Wiersma, Petra S. Kroon, Geert O. R. Janssens, Marcel van Herk, D. C. Hodgson, L. Zadravec Zaletel, C. R. N. Rasch, Arjan Bel, Peter A. N. Bosman, Tanja Alderliesten:
Surrogate-free machine learning-based organ dose reconstruction for pediatric abdominal radiotherapy. CoRR abs/2002.07161 (2020) - [i12]S. C. Maree, Tanja Alderliesten, Peter A. N. Bosman:
Uncrowded Hypervolume-based Multi-objective Optimization with Gene-pool Optimal Mixing. CoRR abs/2004.05068 (2020) - [i11]Tom Den Ottelander, Arkadiy Dushatskiy, Marco Virgolin, Peter A. N. Bosman:
Local Search is a Remarkably Strong Baseline for Neural Architecture Search. CoRR abs/2004.08996 (2020) - [i10]S. C. Maree, Tanja Alderliesten, Peter A. N. Bosman:
Ensuring smoothly navigable approximation sets by Bezier curve parameterizations in evolutionary bi-objective optimization - applied to brachytherapy treatment planning for prostate cancer. CoRR abs/2006.06449 (2020) - [i9]Stefanus C. Maree, Tanja Alderliesten, Peter A. N. Bosman:
Real-valued Evolutionary Multi-modal Multi-objective Optimization by Hill-Valley Clustering. CoRR abs/2010.14998 (2020)
2010 – 2019
- 2019
- [j14]Kleopatra Pirpinia, Peter A. N. Bosman, Jan-Jakob Sonke, Marcel van Herk, Tanja Alderliesten:
Evolutionary Machine Learning for Multi-Objective Class Solutions in Medical Deformable Image Registration. Algorithms 12(5): 99 (2019) - [j13]Ngoc Hoang Luong, Tanja Alderliesten, Bradley R. Pieters, Arjan Bel, Yury Niatsetski, Peter A. N. Bosman:
Fast and insightful bi-objective optimization for prostate cancer treatment planning with high-dose-rate brachytherapy. Appl. Soft Comput. 84 (2019) - [c90]S. C. Maree, Tanja Alderliesten, Peter A. N. Bosman:
Real-valued evolutionary multi-modal multi-objective optimization by hill-valley clustering. GECCO 2019: 568-576 - [c89]Arkadiy Dushatskiy, Adriënne M. Mendrik, Tanja Alderliesten, Peter A. N. Bosman:
Convolutional neural network surrogate-assisted GOMEA. GECCO 2019: 753-761 - [c88]Dirk Thierens, Peter A. N. Bosman:
Model-based evolutionary algorithms. GECCO (Companion) 2019: 806-836 - [c87]Marco Virgolin, Tanja Alderliesten, Peter A. N. Bosman:
Linear scaling with and within semantic backpropagation-based genetic programming for symbolic regression. GECCO 2019: 1084-1092 - [c86]Erik A. Meulman, Peter A. N. Bosman:
Toward self-learning model-based EAs. GECCO (Companion) 2019: 1495-1503 - [c85]Kleopatra Pirpinia, Peter A. N. Bosman, Jan-Jakob Sonke, Marcel van Herk, Tanja 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. Medical Imaging: Image Processing 2019: 1094939 - [i8]Marco Virgolin, Tanja Alderliesten, Cees Witteveen, Peter A. N. Bosman:
A Model-based Genetic Programming Approach for Symbolic Regression of Small Expressions. CoRR abs/1904.02050 (2019) - [i7]Marco Virgolin, Tanja Alderliesten, Peter A. N. Bosman:
On Explaining Machine Learning Models by Evolving Crucial and Compact Features. CoRR abs/1907.02260 (2019) - [i6]S. C. Maree, Tanja Alderliesten, Peter A. N. Bosman:
Benchmarking HillVallEA for the GECCO 2019 Competition on Multimodal Optimization. CoRR abs/1907.10988 (2019) - [i5]Marco Virgolin, Ziyuan Wang, Tanja Alderliesten, Peter A. N. Bosman:
Machine learning for automatic construction of pseudo-realistic pediatric abdominal phantoms. CoRR abs/1909.03723 (2019) - 2018
- [j12]Krzysztof L. Sadowski, Dirk Thierens, Peter A. N. Bosman:
GAMBIT: A Parameterless Model-Based Evolutionary Algorithm for Mixed-Integer Problems. Evol. Comput. 26(1) (2018) - [j11]Ngoc Hoang Luong, Han La Poutré, Peter A. N. Bosman:
Exploiting Linkage Information and Problem-Specific Knowledge in Evolutionary Distribution Network Expansion Planning. Evol. Comput. 26(3) (2018) - [j10]Eric Medvet, Marco Virgolin, Mauro Castelli, Peter A. N. Bosman, Ivo Gonçalves, Tea Tusar:
Unveiling evolutionary algorithm representation with DU maps. Genet. Program. Evolvable Mach. 19(3): 351-389 (2018) - [j9]Peter A. N. Bosman, Marcus 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. Soft Comput. 22(4): 1209-1223 (2018) - [j8]Ngoc Hoang Luong, Tanja Alderliesten, Arjan Bel, Yury Niatsetski, Peter A. N. Bosman:
Application and benchmarking of multi-objective evolutionary algorithms on high-dose-rate brachytherapy planning for prostate cancer treatment. Swarm Evol. Comput. 40: 37-52 (2018) - [j7]Ngoc Hoang Luong, Han La Poutré, Peter A. N. Bosman:
Multi-objective Gene-pool Optimal Mixing Evolutionary Algorithm with the Interleaved Multi-start Scheme. Swarm Evol. Comput. 40: 238-254 (2018) - [c84]Dirk Thierens, Peter A. N. Bosman:
Model-based evolutionary algorithms: GECCO 2018 tutorial. GECCO (Companion) 2018: 553-583 - [c83]Ngoc Hoang Luong, Tanja Alderliesten, Peter A. N. Bosman:
Improving the performance of MO-RV-GOMEA on problems with many objectives using tchebycheff scalarizations. GECCO 2018: 705-712 - [c82]S. C. Maree, Tanja Alderliesten, Dirk Thierens, Peter A. N. Bosman:
Real-valued evolutionary multi-modal optimization driven by hill-valley clustering. GECCO 2018: 857-864 - [c81]Kalia Orphanou, Dirk Thierens, Peter A. N. Bosman:
Learning bayesian network structures with GOMEA. GECCO 2018: 1007-1014 - [c80]Anton Bouter, Tanja Alderliesten, Arjan Bel, Cees Witteveen, Peter A. N. Bosman:
Large-scale parallelization of partial evaluations in evolutionary algorithms for real-world problems. GECCO 2018: 1199-1206 - [c79]Marjolein C. van der Meer, Bradley R. Pieters, Yury Niatsetski, Tanja Alderliesten, Arjan Bel, Peter A. N. Bosman:
Better and faster catheter position optimization in HDR brachytherapy for prostate cancer using multi-objective real-valued GOMEA. GECCO 2018: 1387-1394 - [c78]Marco Virgolin, Tanja Alderliesten, Arjan Bel, Cees Witteveen, Peter A. N. Bosman:
Symbolic regression and feature construction with GP-GOMEA applied to radiotherapy dose reconstruction of childhood cancer survivors. GECCO 2018: 1395-1402 - [c77]G. H. Aalvanger, Ngoc Hoang Luong, Peter A. N. Bosman, Dirk Thierens:
Heuristics in Permutation GOMEA for Solving the Permutation Flowshop Scheduling Problem. PPSN (1) 2018: 146-157 - [i4]S. C. Maree, Tanja Alderliesten, Dirk Thierens, Peter A. N. Bosman:
Benchmarking the Hill-Valley Evolutionary Algorithm for the GECCO 2018 Competition on Niching Methods Multimodal Optimization. CoRR abs/1807.00188 (2018) - [i3]S. C. Maree, Tanja Alderliesten, Dirk Thierens, Peter A. N. Bosman:
Real-Valued Evolutionary Multi-Modal Optimization driven by Hill-Valley Clustering. CoRR abs/1810.07085 (2018) - 2017
- [c76]Anton Bouter, Ngoc Hoang Luong, Cees Witteveen, Tanja Alderliesten, Peter A. N. Bosman:
The multi-objective real-valued gene-pool optimal mixing evolutionary algorithm. GECCO 2017: 537-544 - [c75]Dirk Thierens, Peter A. N. Bosman:
Model-based evolutionary algorithms: GECCO 2017 tutorial. GECCO (Companion) 2017: 545-575 - [c74]Anton Bouter, Tanja Alderliesten, Cees Witteveen, Peter A. N. Bosman:
Exploiting linkage information in real-valued optimization with the real-valued gene-pool optimal mixing evolutionary algorithm. GECCO 2017: 705-712 - [c73]S. C. Maree, Tanja Alderliesten, Dirk Thierens, Peter A. N. Bosman:
Niching an estimation-of-distribution algorithm by hierarchical Gaussian mixture learning. GECCO 2017: 713-720 - [c72]Marco Virgolin, Tanja Alderliesten, Cees Witteveen, Peter A. N. Bosman:
Scalable genetic programming by gene-pool optimal mixing and input-space entropy-based building-block learning. GECCO 2017: 1041-1048 - [c71]Krzysztof L. Sadowski, Marjolein C. van der Meer, Ngoc Hoang Luong, Tanja Alderliesten, Dirk Thierens, Rob van der Laarse, Yury Niatsetski, Arjan Bel, Peter 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. GECCO 2017: 1224-1231 - [c70]Ngoc Hoang Luong, Anton Bouter, Marjolein C. van der Meer, Yury Niatsetski, Cees Witteveen, Arjan Bel, Tanja Alderliesten, Peter 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. GECCO (Companion) 2017: 1372-1379 - [c69]Anton Bouter, Kleopatra Pirpinia, Tanja Alderliesten, Peter A. N. Bosman:
Spatial redistribution of irregularly-spaced pareto fronts for more intuitive navigation and solution selection. GECCO (Companion) 2017: 1697-1704 - [c68]Anton Bouter, Tanja Alderliesten, Peter 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. Medical Imaging: Image Processing 2017: 1013312 - [e2]Peter A. N. Bosman:
Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2017, Berlin, Germany, July 15-19, 2017. ACM 2017, ISBN 978-1-4503-4920-8 [contents] - [e1]Peter A. N. Bosman:
Genetic and Evolutionary Computation Conference, Berlin, Germany, July 15-19, 2017, Companion Material Proceedings. ACM 2017, ISBN 978-1-4503-4939-0 [contents] - 2016
- [c67]Krzysztof L. Sadowski, Peter A. N. Bosman, Dirk Thierens:
Learning and exploiting mixed variable dependencies with a model-based EA. CEC 2016: 4382-4389 - [c66]Dirk Thierens, Peter A. N. Bosman:
Model-Based Evolutionary Algorithms. GECCO (Companion) 2016: 385-412 - [c65]Peter A. N. Bosman, Ngoc Hoang Luong, Dirk Thierens:
Expanding from Discrete Cartesian to Permutation Gene-pool Optimal Mixing Evolutionary Algorithms. GECCO 2016: 637-644 - [c64]Peter A. N. Bosman, John A. W. McCall:
GECCO'16 Model-Based Evolutionary Algorithms (MBEA) Workshop Chairs' Welcome. GECCO (Companion) 2016: 1401 - [c63]Kleopatra Pirpinia, Peter A. N. Bosman, Jan-Jakob Sonke, Marcel van Herk, Tanja Alderliesten:
A first step toward uncovering the truth about weight tuning in deformable image registration. Medical Imaging: Image Processing 2016: 978445 - [c62]Peter A. N. Bosman, Tanja 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. Medical Imaging: Image Processing 2016: 978447 - [c61]Willem den Besten, Dirk Thierens, Peter A. N. Bosman:
The Multiple Insertion Pyramid: A Fast Parameter-Less Population Scheme. PPSN 2016: 48-58 - 2015
- [c60]Dirk Thierens, Peter A. N. Bosman:
Model-Based Evolutionary Algorithms. GECCO (Companion) 2015: 93-120 - [c59]Krzysztof L. Sadowski, Peter A. N. Bosman, Dirk Thierens:
A Clustering-Based Model-Building EA for Optimization Problems with Binary and Real-Valued Variables. GECCO 2015: 911-918 - [c58]Ngoc Hoang Luong, Han La Poutré, Peter A. N. Bosman:
Exploiting Linkage Information and Problem-Specific Knowledge in Evolutionary Distribution Network Expansion Planning. GECCO 2015: 1231-1238 - [c57]Kleopatra Pirpinia, Tanja Alderliesten, Jan-Jakob Sonke, Marcel van Herk, Peter A. N. Bosman:
Diversifying Multi-Objective Gradient Techniques and their Role in Hybrid Multi-Objective Evolutionary Algorithms for Deformable Medical Image Registration. GECCO 2015: 1255-1262 - [c56]Roy de Bokx, Dirk Thierens, Peter A. N. Bosman:
In Search of Optimal Linkage Trees. GECCO (Companion) 2015: 1375-1376 - [c55]Tanja Alderliesten, Peter A. N. Bosman, Arjan Bel:
Getting the most out of additional guidance information in deformable image registration by leveraging multi-objective optimization. Medical Imaging: Image Processing 2015: 94131R - [c54]Kleopatra Pirpinia, Peter A. N. Bosman, Jan-Jakob Sonke, Marcel van Herk, Tanja 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. Medical Imaging: Image Processing 2015: 941339 - 2014
- [c53]Ngoc Hoang Luong, Han La Poutré, Peter A. N. Bosman:
Multi-objective gene-pool optimal mixing evolutionary algorithms. GECCO 2014: 357-364 - [c52]Dirk Thierens, Peter A. N. Bosman:
Model-based evolutionary algorithms. GECCO (Companion) 2014: 431-458 - [c51]Sílvio Miguel Fragoso Rodrigues, Pavol Bauer, Peter A. N. Bosman:
A novel population-based multi-objective CMA-ES and the impact of different constraint handling techniques. GECCO 2014: 991-998 - [c50]Ngoc Hoang Luong, Marinus O. W. Grond, Han La Poutré, Peter A. N. Bosman:
Efficiency enhancements for evolutionary capacity planning in distribution grids. GECCO (Companion) 2014: 1189-1196 - [c49]Tanja Alderliesten, Peter A. N. Bosman, Jan-Jakob Sonke, Arjan Bel:
A multi-resolution strategy for a multi-objective deformable image registration framework that accommodates large anatomical differences. Medical Imaging: Image Processing 2014: 90343G - [c48]Bart Liefers, Felix Claessen, Eric J. Pauwels, Peter A. N. Bosman, Han La Poutré:
Market Garden: A Simulation Environment for Research and User Experience in Smart Grids. PAAMS 2014: 351-354 - [c47]Krzysztof L. Sadowski, Dirk Thierens, Peter A. N. Bosman:
Combining Model-Based EAs for Mixed-Integer Problems. PPSN 2014: 342-351 - [c46]Marinus O. W. Grond, Ngoc Hoang Luong, Johan Morren, Peter A. N. Bosman, Han Slootweg, Han La Poutré:
Practice-oriented optimization of distribution network planning using metaheuristic algorithms. PSCC 2014: 1-8 - 2013
- [j6]Peter A. N. Bosman, Jörn Grahl, Dirk Thierens:
Benchmarking Parameter-Free AMaLGaM on Functions With and Without Noise. Evol. Comput. 21(3): 445-469 (2013) - [c45]Hoang N. Luong, Marinus O. W. Grond, Peter A. N. Bosman, Han La Poutré:
Medium-Voltage Distribution Network Expansion Planning with Gene-pool Optimal Mixing Evolutionary Algorithms. Artificial Evolution 2013: 93-105 - [c44]Peter A. N. Bosman, Dirk Thierens:
More concise and robust linkage learning by filtering and combining linkage hierarchies. GECCO 2013: 359-366 - [c43]Dirk Thierens, Peter A. N. Bosman:
Model-based evolutionary algorithms. GECCO (Companion) 2013: 377-404 - [c42]Krzysztof L. Sadowski, Peter A. N. Bosman, Dirk Thierens:
On the usefulness of linkage processing for solving MAX-SAT. GECCO 2013: 853-860 - [c41]Dirk Thierens, Peter A. N. Bosman:
Hierarchical problem solving with the linkage tree genetic algorithm. GECCO 2013: 877-884 - [c40]Tim Brys, Madalina M. Drugan, Peter A. N. Bosman, Martine De Cock, Ann Nowé:
Solving satisfiability in fuzzy logics by mixing CMA-ES. GECCO 2013: 1125-1132 - [c39]Tim Brys, Madalina M. Drugan, Peter A. N. Bosman, Martine De Cock, Ann Nowé:
Local search and restart strategies for satisfiability solving in fuzzy logics. GEFS 2013: 52-59 - [c38]Tanja Alderliesten, Jan-Jakob Sonke, Peter A. N. Bosman:
Deformable image registration by multi-objective optimization using a dual-dynamic transformation model to account for large anatomical differences. Medical Imaging: Image Processing 2013: 866910 - [i2]Felix Claessen, Nicolas Höning, Bart Liefers, Han La Poutré, Peter A. N. Bosman:
Market Garden: A Scalable Research Environment for Heterogeneous Electricity Markets. ERCIM News 2013(92) (2013) - 2012
- [j5]Peter A. N. Bosman:
On Gradients and Hybrid Evolutionary Algorithms for Real-Valued Multiobjective Optimization. IEEE Trans. Evol. Comput. 16(1): 51-69 (2012) - [c37]Peter A. N. Bosman, Tanja Alderliesten:
Incremental gaussian model-building in multi-objective EDAs with an application to deformable image registration. GECCO 2012: 241-248 - [c36]Dirk Thierens, Peter A. N. Bosman:
Predetermined versus learned linkage models. GECCO 2012: 289-296 - [c35]Peter A. N. Bosman, Dirk Thierens:
Linkage neighbors, optimal mixing and forced improvements in genetic algorithms. GECCO 2012: 585-592 - [c34]Dirk Thierens, Peter A. N. Bosman:
Learning the Neighborhood with the Linkage Tree Genetic Algorithm. LION 2012: 491-496 - [c33]Tanja Alderliesten, Jan-Jakob Sonke, Peter A. N. Bosman:
Multi-objective optimization for deformable image registration: proof of concept. Medical Imaging: Image Processing 2012: 831420 - [c32]Hoang N. Luong, Peter A. N. Bosman:
Elitist Archiving for Multi-Objective Evolutionary Algorithms: To Adapt or Not to Adapt. PPSN (2) 2012: 72-81 - [c31]Peter A. N. Bosman, Dirk Thierens:
On Measures to Build Linkage Trees in LTGA. PPSN (1) 2012: 276-285 - [c30]Dirk Thierens, Peter A. N. Bosman:
Evolvability Analysis of the Linkage Tree Genetic Algorithm. PPSN (1) 2012: 286-295 - 2011
- [c29]Dirk Thierens, Peter A. N. Bosman:
Optimal mixing evolutionary algorithms. GECCO 2011: 617-624 - [c28]Peter A. N. Bosman, Dirk Thierens:
The roles of local search, model building and optimal mixing in evolutionary algorithms from a bbo perspective. GECCO (Companion) 2011: 663-670 - [c27]Sara Ramezani, Peter A. N. Bosman, Han La Poutré:
Adaptive Strategies for Dynamic Pricing Agents. IAT 2011: 323-328 - 2010
- [c26]Peter A. N. Bosman:
The anticipated mean shift and cluster registration in mixture-based EDAs for multi-objective optimization. GECCO 2010: 351-358 - [c25]Anke K. Hutzschenreuter, Peter A. N. Bosman, Han La Poutré:
Enhanced hospital resource management using anticipatory policies in online dynamic multi-objective optimization. GECCO 2010: 541-542 - [i1]Anke K. Hutzschenreuter, Peter A. N. Bosman, Han La Poutré:
A Computational Approach to Patient Flow Logistics in Hospitals. ERCIM News 2010(81) (2010)
2000 – 2009
- 2009
- [c24]Ivan B. Vermeulen, Sander M. Bohté, Peter A. N. Bosman, Sylvia G. Elkhuizen, Piet J. M. Bakker, Johannes A. La Poutré:
Optimization of Online Patient Scheduling with Urgencies and Preferences. AIME 2009: 71-80 - [c23]Anke K. Hutzschenreuter, Peter A. N. Bosman, Han La Poutré:
Evolutionary Multiobjective Optimization for Dynamic Hospital Resource Management. EMO 2009: 320-334 - [c22]Peter A. N. Bosman:
On empirical memory design, faster selection of bayesian factorizations and parameter-free gaussian EDAs. GECCO 2009: 389-396 - [c21]Peter A. N. Bosman, Jörn Grahl, Dirk Thierens:
AMaLGaM IDEAs in noiseless black-box optimization benchmarking. GECCO (Companion) 2009: 2247-2254 - [c20]Peter A. N. Bosman, Jörn Grahl, Dirk Thierens:
AMaLGaM IDEAs in noisy black-box optimization benchmarking. GECCO (Companion) 2009: 2351-2358 - 2008
- [j4]Peter A. N. Bosman, Jörn Grahl:
Matching inductive search bias and problem structure in continuous Estimation-of-Distribution Algorithms. Eur. J. Oper. Res. 185(3): 1246-1264 (2008) - [c19]Anke K. Hutzschenreuter, Peter A. N. Bosman, Ilona Blonk-Altena, Jan van Aarle, Han La Poutré:
Agent-based patient admission scheduling in hospitals. AAMAS (Industry Track) 2008: 45-52 - [c18]Peter A. N. Bosman, Jörn Grahl, Dirk Thierens:
Enhancing the Performance of Maximum-Likelihood Gaussian EDAs Using Anticipated Mean Shift. PPSN 2008: 133-143 - [p4]Jörn Grahl, Stefan Minner, Peter A. N. Bosman:
Learning Structure Illuminates Black Boxes - An Introduction to Estimation of Distribution Algorithms. Advances in Metaheuristics for Hard Optimization 2008: 365-395 - 2007
- [j3]Tanja Alderliesten, Peter A. N. Bosman, Wiro J. Niessen:
Towards a Real-Time Minimally-Invasive Vascular Intervention Simulation System. IEEE Trans. Medical Imaging 26(1): 128-132 (2007) - [c17]Peter A. N. Bosman, Han La Poutré:
Inventory management and the impact of anticipation in evolutionary stochastic online dynamic optimization. IEEE Congress on Evolutionary Computation 2007: 268-275 - [c16]Peter A. N. Bosman, Jörn Grahl, Franz Rothlauf:
SDR: a better trigger for adaptive variance scaling in normal EDAs. GECCO 2007: 492-499 - [c15]Peter A. N. Bosman, Dirk Thierens:
Adaptive variance scaling in continuous multi-objective estimation-of-distribution algorithms. GECCO 2007: 500-507 - [c14]Jörn Grahl, Peter A. N. Bosman, Stefan Minner:
Convergence phases, variance trajectories, and runtime analysis of continuous EDAs. GECCO 2007: 516-522 - [c13]Peter A. N. Bosman, Han La Poutré:
Learning and anticipation in online dynamic optimization with evolutionary algorithms: the stochastic case. GECCO 2007: 1165-1172 - [p3]Peter A. N. Bosman:
Learning and Anticipation in Online Dynamic Optimization. Evolutionary Computation in Dynamic and Uncertain Environments 2007: 129-152 - 2006
- [c12]Jörn Grahl, Peter A. N. Bosman, Franz Rothlauf:
The correlation-triggered adaptive variance scaling IDEA. GECCO 2006: 397-404 - [c11]Peter A. N. Bosman, Edwin D. de Jong:
Combining gradient techniques for numerical multi-objective evolutionary optimization. GECCO 2006: 627-634 - [c10]Peter A. N. Bosman, Han La Poutré:
Computationally Intelligent Online Dynamic Vehicle Routing by Explicit Load Prediction in an Evolutionary Algorithm. PPSN 2006: 312-321 - [p2]Peter A. N. Bosman, Dirk Thierens:
Multi-objective Optimization with the Naive 𝕄 ID 𝔼 A. Towards a New Evolutionary Computation 2006: 123-157 - [p1]Peter A. N. Bosman, Dirk Thierens:
Numerical Optimization with Real-Valued Estimation-of-Distribution Algorithms. Scalable Optimization via Probabilistic Modeling 2006: 91-120 - 2005
- [c9]Peter A. N. Bosman:
Learning, Anticipation and Time-Deception in Evolutionary Online Dynamic Optimization. BNAIC 2005: 321-322 - [c8]Peter A. N. Bosman, Dirk Thierens:
The Naive MIDEA: A Baseline Multi-objective EA. EMO 2005: 428-442 - [c7]Peter A. N. Bosman:
Learning, anticipation and time-deception in evolutionary online dynamic optimization. GECCO Workshops 2005: 39-47 - [c6]Peter A. N. Bosman, Tanja Alderliesten:
Evolutionary algorithms for medical simulations: a case study in minimally-invasive vascular interventions. GECCO Workshops 2005: 125-132 - [c5]Peter A. N. Bosman, Edwin D. de Jong:
Exploiting gradient information in numerical multi--objective evolutionary optimization. GECCO 2005: 755-762 - 2004
- [c4]Peter A. N. Bosman, Edwin D. de Jong:
Learning Probabilistic Tree Grammars for Genetic Programming. PPSN 2004: 192-201 - 2003
- [j2]Peter A. N. Bosman, Dirk Thierens:
The balance between proximity and diversity in multiobjective evolutionary algorithms. IEEE Trans. Evol. Comput. 7(2): 174-188 (2003) - 2002
- [j1]Peter A. N. Bosman, Dirk Thierens:
Multi-objective optimization with diversity preserving mixture-based iterated density estimation evolutionary algorithms. Int. J. Approx. Reason. 31(3): 259-289 (2002) - [c3]Peter A. N. Bosman, Dirk Thierens:
Permutation Optimization by Iterated Estimation of Random Keys Marginal Product Factorizations. PPSN 2002: 331-340 - 2000
- [c2]Peter A. N. Bosman, Dirk Thierens:
Expanding from Discrete to Continuous Estimation of Distribution Algorithms: The IDEA. PPSN 2000: 767-776
1990 – 1999
- 1999
- [c1]Peter A. N. Bosman, Dirk Thierens:
Linkage Information Processing In Distribution Estimation Algorithms. GECCO 1999: 60-67
Coauthor Index
[c139] [c138] [c137] [c136] [c135] [c133] [c132] [c131] [i53] [i52] [i51] [i50] [i49] [i48] [i47] [i46] [i45] [i44] [i43] [c130] [c129] [c128] [c126] [c125] [c123] [c122] [c121] [c120] [i42] [i41] [i40] [i39] [i37] [i36] [j21] [c119] [c118] [c116] [c115] [c114] [c112] [c111] [c109] [c108] [c107] [c106] [c104] [d1] [i34] [i33] [i32] [i31] [i30] [i29] [i28] [i27] [i26] [i24] [i23] [i22] [i21] [j19] [j18] [j16] [c103] [c99] [p5] [i19] [i18] [i17] [j15] [c96] [c95] [c94] [c93] [c92] [c91] [i15] [i14] [i13] [i12] [i10] [i9] [j14] [j13] [c90] [c89] [c87] [c85] [i8] [i7] [i6] [i5] [j8] [c83] [c82] [c80] [c79] [c78] [i4] [i3] [c76] [c74] [c73] [c72] [c71] [c70] [c69] [c68] [c63] [c62] [c57] [c55] [c54] [c49] [c38] [c37] [c33] [j3] [c6]
Ngoc Hoang Luong
aka: Hoang N. Luong
aka: Hoang N. Luong
Stefanus C. Maree
aka: S. C. Maree
aka: S. C. Maree
Han La Poutré
aka: Johannes A. La Poutré
aka: Johannes A. La Poutré
[c138] [c134] [i46] [c128] [c127] [i39] [c116] [c113] [d1] [i26] [c101] [c100] [c98] [p5] [i16] [c97] [c88] [j12] [c84] [c82] [c81] [c77] [i4] [i3] [c75] [c73] [c71] [c67] [c66] [c65] [c61] [c60] [c59] [c56] [c52] [c47] [j6] [c44] [c43] [c42] [c41] [c36] [c35] [c34] [c31] [c30] [c29] [c28] [c21] [c20] [c18] [c15] [p2] [p1] [c8] [j2] [j1] [c3] [c2] [c1]
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-10-30 21:34 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
