Optimal Mixing Evolutionary Algorithms for Large-Scale Real-Valued Optimization

Including Real-World Medical Applications

Doctoral thesis (2023)

Authors

P.A. Bouter Centrum Wiskunde & Informatica (CWI), Algorithmics - EEMCS
Research Group
Algorithmics (EEMCS) (TU Delft)
Copyright: © 2023 P.A. Bouter
DOI: https://doi.org/10.4233/uuid:0e03913c-898e-4392-8de5-072a7ead7fd6
CUDA Evolutionary Algorithms Gene-pool Optimal Mixing Gray-box optimization Large-scale optimization Real-valued optimization Graphics Processing Unit (GPU) Brachytherapy Treatment planning Deformable image registration Multi-objective Optimisation
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Published Date

2023

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Software Technology

Research Group

Algorithmics

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.