Learning to forget : design of experiments for line-based Bayesian optimization in dynamic environments
- Author
- Jens Jocqué, Tom Van Steenkiste, Pieter Stroobant, Rémi Delanghe, Dirk Deschrijver (UGent) and Tom Dhaene (UGent)
- Organization
- Abstract
- Various scientific and engineering fields rely on measurements in 2D spaces to generate a map or locate the global optimum. Traditional design of experiments methods determine the measurement locations upfront, while a sequential approach iteratively extends the design. Typically, the cost of traveling between sample locations can be ignored, for example in simulation experiments. In those cases, the experimental design is generated using a point-based method. However, if traveling towards the next sample location incurs an additional cost, line-based sampling methods are favored. In this setting, the sampling algorithm needs to generate a route of easurement locations. A common engineering problem is locating the global optimum. In certain cases, such as fire hotspot monitoring, the location of the optimum dynamically changes. In this work, an algorithm is proposed for sequentially locating dynamic optima in a line-based setting. The algorithm is evaluated on two dynamic optimization benchmark problems.
- Keywords
- TRAJECTORIES, COVERAGE
Downloads
-
(...).pdf
- full text (Published version)
- |
- UGent only
- |
- |
- 905.08 KB
Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8620383
- MLA
- Jocqué, Jens, et al. “Learning to Forget : Design of Experiments for Line-Based Bayesian Optimization in Dynamic Environments.” 2019 WINTER SIMULATION CONFERENCE (WSC), IEEE, 2019, pp. 656–67, doi:10.1109/WSC40007.2019.9004673.
- APA
- Jocqué, J., Van Steenkiste, T., Stroobant, P., Delanghe, R., Deschrijver, D., & Dhaene, T. (2019). Learning to forget : design of experiments for line-based Bayesian optimization in dynamic environments. 2019 WINTER SIMULATION CONFERENCE (WSC), 656–667. https://doi.org/10.1109/WSC40007.2019.9004673
- Chicago author-date
- Jocqué, Jens, Tom Van Steenkiste, Pieter Stroobant, Rémi Delanghe, Dirk Deschrijver, and Tom Dhaene. 2019. “Learning to Forget : Design of Experiments for Line-Based Bayesian Optimization in Dynamic Environments.” In 2019 WINTER SIMULATION CONFERENCE (WSC), 656–67. New York: IEEE. https://doi.org/10.1109/WSC40007.2019.9004673.
- Chicago author-date (all authors)
- Jocqué, Jens, Tom Van Steenkiste, Pieter Stroobant, Rémi Delanghe, Dirk Deschrijver, and Tom Dhaene. 2019. “Learning to Forget : Design of Experiments for Line-Based Bayesian Optimization in Dynamic Environments.” In 2019 WINTER SIMULATION CONFERENCE (WSC), 656–667. New York: IEEE. doi:10.1109/WSC40007.2019.9004673.
- Vancouver
- 1.Jocqué J, Van Steenkiste T, Stroobant P, Delanghe R, Deschrijver D, Dhaene T. Learning to forget : design of experiments for line-based Bayesian optimization in dynamic environments. In: 2019 WINTER SIMULATION CONFERENCE (WSC). New York: IEEE; 2019. p. 656–67.
- IEEE
- [1]J. Jocqué, T. Van Steenkiste, P. Stroobant, R. Delanghe, D. Deschrijver, and T. Dhaene, “Learning to forget : design of experiments for line-based Bayesian optimization in dynamic environments,” in 2019 WINTER SIMULATION CONFERENCE (WSC), National Harbor, USA, 2019, pp. 656–667.
@inproceedings{8620383,
abstract = {{Various scientific and engineering fields rely on measurements in 2D spaces to generate a map or locate the global optimum. Traditional design of experiments methods determine the measurement locations upfront, while a sequential approach iteratively extends the design. Typically, the cost of traveling between sample locations can be ignored, for example in simulation experiments. In those cases, the experimental design is generated using a point-based method. However, if traveling towards the next sample location incurs an additional cost, line-based sampling methods are favored. In this setting, the sampling algorithm needs to generate a route of easurement locations. A common engineering problem is locating the global optimum. In certain cases, such as fire hotspot monitoring, the location of the optimum dynamically changes. In this work, an algorithm is proposed for sequentially locating dynamic optima in a line-based setting. The algorithm is evaluated on two dynamic optimization benchmark problems.}},
author = {{Jocqué, Jens and Van Steenkiste, Tom and Stroobant, Pieter and Delanghe, Rémi and Deschrijver, Dirk and Dhaene, Tom}},
booktitle = {{2019 WINTER SIMULATION CONFERENCE (WSC)}},
isbn = {{9781728132839}},
issn = {{0891-7736}},
keywords = {{TRAJECTORIES,COVERAGE}},
language = {{eng}},
location = {{National Harbor, USA}},
pages = {{656--667}},
publisher = {{IEEE}},
title = {{Learning to forget : design of experiments for line-based Bayesian optimization in dynamic environments}},
url = {{http://dx.doi.org/10.1109/WSC40007.2019.9004673}},
year = {{2019}},
}
- Altmetric
- View in Altmetric
- Web of Science
- Times cited: