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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. 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. 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. 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. Image Processing 2022 - [c107]Arkadiy Dushatskiy, Gerry Lowe, Peter A. N. Bosman, Tanja Alderliesten:
Data variation-aware medical image segmentation. 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. 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. 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. 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]