Parallel cost-aware optimization of multidimensional black-box functions

Bachelor Thesis (2023)
Author(s)

O. Sihlovec (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Contributor(s)

Matthijs T. J. Spaan – Mentor (TU Delft - Algorithmics)

J.A. de Vries – Mentor (TU Delft - Algorithmics)

C. Lofi – Graduation committee member (TU Delft - Web Information Systems)

Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright
© 2023 Oliver Sihlovec
More Info
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Publication Year
2023
Language
English
Copyright
© 2023 Oliver Sihlovec
Graduation Date
28-05-2023
Awarding Institution
Delft University of Technology
Project
CSE3000 Research Project
Programme
Computer Science and Engineering
Related content

This link redirects to the student's code repository containing the code, which has been utilized throughout the research and which can be used to reproduce the results outlined in the paper.

https://github.com/osihlovec/ca-qei
Faculty
Electrical Engineering, Mathematics and Computer Science
Reuse Rights

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Abstract

Scientific problems are often concerned with optimization of control variables of complex systems, for instance hyperparameters of machine learning models. A popular solution for such intractable environments is Bayesian optimization. However, many implementations disregard dynamic evaluation costs associated with the optimization procedure. Furthermore, another common trope among
Bayesian algorithms is that they are short-sighted and do not consider long-term effects of their actions. This paper investigates the viability of multitimestep cost-aware Bayesian optimizers and evaluates their performance in environments with delayed rewards. To this end, we combine existing works on parallel Bayesian optimizers and costaware heuristics. Our findings reveal that although
such parallel optimizers yield more optimal results and are more resistant to delayed feedback compared to their myopic counterparts, they are unable to achieve cost-awareness.

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