Optimal Mixing Evolutionary Algorithms for Large-Scale Real-Valued Optimization: Including Real-World Medical Applications

Anton Bouter*

*Corresponding author for this work

Research output: ThesisDissertation (TU Delft)

57 Downloads (Pure)

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.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • Delft University of Technology
Supervisors/Advisors
  • Bosman, P.A.N., Supervisor
  • Alderliesten, T., Advisor
Award date13 Feb 2023
Print ISBNs978-94-6366-648-0
DOIs
Publication statusPublished - 2023

Keywords

  • Evolutionary Algorithms
  • Gene-pool Optimal Mixing
  • Gray-box optimization
  • Large-scale optimization
  • Real-valued optimization
  • Multi-objective Optimisation
  • Graphics Processing Unit (GPU)
  • CUDA
  • Brachytherapy
  • Treatment planning
  • Deformable image registration

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