Search Results - "Bosman, P A N"

Surrogate-free machine learning-based organ dose reconstruction for pediatric abdominal radiotherapy by Virgolin, M, Wang, Z, Balgobind, B V, van Dijk, I W E M, Wiersma, J, Kroon, P S, Janssens, G O, van Herk, M, Hodgson, D C, Zadravec Zaletel, L, Rasch, C R N, Bel, A, Bosman, P A N, Alderliesten, T

“…To study radiotherapy-related adverse effects, detailed dose information (3D distribution) is needed for accurate dose-effect modeling. For childhood cancer…”
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Temporal True and Surrogate Fitness Landscape Analysis for Expensive Bi-Objective Optimisation by Rodriguez, C. J, Thomson, S. L, Alderliesten, T, Bosman, P. A. N

“…Many real-world problems have expensive-to-compute fitness functions and are multi-objective in nature. Surrogate-assisted evolutionary algorithms are often…”
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Real-Valued Evolutionary Multi-Modal Optimization driven by Hill-Valley Clustering by Maree, S. C, Alderliesten, T, Thierens, D, Bosman, P. A. N

“…In Proceedings of the Genetic and Evolutionary Computation Conference 2018, GECCO-2018, July 15-19, 2018, Kyoto, Japan. ACM, New York, NY, USA Model-based…”
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On Gradients and Hybrid Evolutionary Algorithms for Real-Valued Multiobjective Optimization by Bosman, P. A. N.

“…Algorithms that make use of the gradient, i.e., the direction of maximum improvement, to search for the optimum of a single-objective function have been around…”
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Surrogate-free machine learning-based organ dose reconstruction for pediatric abdominal radiotherapy by Virgolin, M, Wang, Z, Balgobind, B. V, van Dijk, I. W. E. M, Wiersma, J, Kroon, P. S, Janssens, G. O, van Herk, M, Hodgson, D. C, Zaletel, L. Zadravec, Rasch, C. R. N, Bel, A, Bosman, P. A. N, Alderliesten, T

“…Physics in Medicine & Biology. 2020 Dec 8;65(24):245021 To study radiotherapy-related adverse effects, detailed dose information (3D distribution) is needed…”
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The balance between proximity and diversity in multiobjective evolutionary algorithms by Bosman, P.A.N., Thierens, D.

“…Over the last decade, a variety of evolutionary algorithms (EAs) have been proposed for solving multiobjective optimization problems. Especially more recent…”
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Deep learning-based auto-segmentation of paraganglioma for growth monitoring by Sijben, E. M. C, Jansen, J. C, de Ridder, M, Bosman, P. A. N, Alderliesten, T

“…Volume measurement of a paraganglioma (a rare neuroendocrine tumor that typically forms along major blood vessels and nerve pathways in the head and neck…”
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Function Class Learning with Genetic Programming: Towards Explainable Meta Learning for Tumor Growth Functionals by Sijben, E. M. C, Jansen, J. C, Bosman, P. A. N, Alderliesten, T

“…Paragangliomas are rare, primarily slow-growing tumors for which the underlying growth pattern is unknown. Therefore, determining the best care for a patient…”
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Multi-modal multi-objective model-based genetic programming to find multiple diverse high-quality models by Sijben, E. M. C, Alderliesten, T, Bosman, P. A. N

“…Explainable artificial intelligence (XAI) is an important and rapidly expanding research topic. The goal of XAI is to gain trust in a machine learning (ML)…”
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Real-valued Evolutionary Multi-modal Multi-objective Optimization by Hill-Valley Clustering by Maree, S. C, Alderliesten, T, Bosman, P. A. N

“…In model-based evolutionary algorithms (EAs), the underlying search distribution is adapted to the problem at hand, for example based on dependencies between…”
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Ensuring smoothly navigable approximation sets by Bezier curve parameterizations in evolutionary bi-objective optimization -- applied to brachytherapy treatment planning for prostate cancer by Maree, S. C, Alderliesten, T, Bosman, P. A. N

“…The aim of bi-objective optimization is to obtain an approximation set of (near) Pareto optimal solutions. A decision maker then navigates this set to select a…”
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Uncrowded Hypervolume-based Multi-objective Optimization with Gene-pool Optimal Mixing by Maree, S. C, Alderliesten, T, Bosman, P. A. N

“…Domination-based multi-objective (MO) evolutionary algorithms (EAs) are today arguably the most frequently used type of MOEA. These methods however stagnate…”
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Benchmarking HillVallEA for the GECCO 2019 Competition on Multimodal Optimization by Maree, S. C, Alderliesten, T, Bosman, P. A. N

“…This report presents benchmarking results of the Hill-Valley Evolutionary Algorithm version 2019 (HillVallEA19) on the CEC2013 niching benchmark suite under…”
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Local Search is a Remarkably Strong Baseline for Neural Architecture Search by Ottelander, T. Den, Dushatskiy, A, Virgolin, M, Bosman, P. A. N

“…Neural Architecture Search (NAS), i.e., the automation of neural network design, has gained much popularity in recent years with increasingly complex search…”
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Inventory management and the impact of anticipation in evolutionary stochastic online dynamic optimization by Bosman, P.A.N., La Poutre, H.

“…Inventory management (IM) is an important area in logistics. The goal is to manage the inventory of a vendor as efficiently as possible. Its practical…”
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Benchmarking the Hill-Valley Evolutionary Algorithm for the GECCO 2018 Competition on Niching Methods Multimodal Optimization by Maree, S. C, Alderliesten, T, Thierens, D, Bosman, P. A. N

“…This report presents benchmarking results of the latest version of the Hill-Valley Evolutionary Algorithm (HillVallEA) on the CEC2013 niching benchmark suite…”
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Adaptive Strategies for Dynamic Pricing Agents by Ramezani, S., Bosman, P. A. N., La Poutre, H.

“…Dynamic Pricing (DyP) is a form of Revenue Management in which the price of a (usually) perishable good is changed over time to increase revenue. It is an…”
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