Arthur Guijt

Full Name

A. Guijt

Arthur.Guijt@cwi.nl

Phone

+31 20 592 4073

Room

M231

Department(s)

Evolutionary Intelligence

Function(s)

PhD student

Biography

Arthur Guijt is a PhD student working on the DAEDALUS project. Arthur has obtained both his Bachelor and Master in Computer Science in Delft. His master's thesis was on solving permutation problems using evolutionary algorithms, such as satellite scheduling problems. His work as a PhD student focusses on making evolutionary algorithms - in particular GOMEA - more performant for Neural Architecture search.

Guijt, A., Thierens, D., Alderliesten, T., & Bosman, P. (2024). Exploring the search space of neural network combinations obtained with efficient model stitching. In GECCO 2024 - Proceedings of the 2024 Genetic and Evolutionary Computation Conference (pp. 1914–1923). doi:10.1145/3638530.3664131
Guijt, A., Thierens, D., Alderliesten, T., & Bosman, P. (2024). Exploring the Search Space of Neural Network Combinations obtained with Efficient Model Stitching - Results Data. doi:10.5281/zenodo.11120103
Guijt, A., Thierens, D., Alderliesten, T., & Bosman, P. (2023). The impact of asynchrony on parallel model-based EAs. In GECCO '23: Proceedings of the 2023 Genetic and Evolutionary Computation Conference (pp. 910–918). doi:10.1145/3583131.3590406
Guijt, A., Thierens, D., Alderliesten, T., & Bosman, P. (2022). Solving multi-structured problems by introducing linkage kernels into GOMEA. In GECCO 2022 - Proceedings of the 2022 Genetic and Evolutionary Computation Conference (pp. 703–711). doi:https://dl.acm.org/doi/pdf/10.1145/3512290.3528828
Guijt, A., Luong, H., Bosman, P., & de Weerdt, M. (2022). On the impact of linkage learning, gene-pool optimal mixing, and non-redundant encoding on permutation optimization. Swarm and Evolutionary Computation, 70, 101044:1–101044:13. doi:10.1016/j.swevo.2022.101044
Bliek, L., Guijt, A., & Karlsson, R. (2021). Hospital simulation model optimisation with a random ReLU expansion surrogate model. In GECCO 2021 Proceedings (pp. 13–14). doi:10.1145/3449726.3463279
Bliek, L., Guijt, A., Verwer, S., & de Weerdt, M. (2021). Black-box mixed-variable optimisation using a surrogate model that satisfies integer constraints. In GECCO 2021 Proceedings (pp. 1851–1859). doi:10.1145/3449726.3463136

Distributed and Automated Evolutionary Deep Architecture Learning with Unprecedented Scalability (DAEDALUS)

Arthur Guijt

Biography

Publications

Current projects with external funding

Arthur Guijt

Biography

Publications

Current projects with external funding

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