Print Email Facebook Twitter Optimal Mixing Evolutionary Algorithms for Large-Scale Real-Valued Optimization Title Optimal Mixing Evolutionary Algorithms for Large-Scale Real-Valued Optimization: Including Real-World Medical Applications Author Bouter, P.A. (TU Delft Algorithmics; Centrum Wiskunde & Informatica (CWI)) Contributor Bosman, P.A.N. (promotor) Alderliesten, T. (copromotor) Degree granting institution Delft University of Technology Date 2023-02-13 Abstract In recent years, the use of Artificial Intelligence (AI) has become prevalent in a large number of societally relevant, real-world problems, e.g., in the domains of engineering and health care. The field of Evolutionary Computation (EC) can be considered to be a sub-field of AI, concerning optimization using Evolutionary Algorithms (EAs), which are population-based (meta-)heuristics that employ the Darwinian principles of evolution, i.e., variation and selection. Such EAs are historically mainly considered for the optimization of difficult, non-linear problems in a Black-Box Optimization (BBO) setting, because EAs can effectively optimize such problems even when very little is known about the optimization problem and its structure. This is in contrast to optimization methods that are specifically designed for certain problems of which the definition and structure are known, i.e., a White-Box Optimization (WBO) setting. Subject Evolutionary AlgorithmsGene-pool Optimal MixingGray-box optimizationLarge-scale optimizationReal-valued optimizationMulti-objective OptimisationGraphics Processing Unit (GPU)CUDABrachytherapyTreatment planningDeformable image registration To reference this document use: https://doi.org/10.4233/uuid:0e03913c-898e-4392-8de5-072a7ead7fd6 ISBN 978-94-6366-648-0 Part of collection Institutional Repository Document type doctoral thesis Rights © 2023 P.A. Bouter Files PDF dissertation_antonbouter.pdf 101.57 MB Close viewer /islandora/object/uuid:0e03913c-898e-4392-8de5-072a7ead7fd6/datastream/OBJ/view