Machine learning for automated EEG-based biomarkers of cognitive impairment during Deep Brain Stimulation screening in patients with Parkinson’s Disease

Autor: Geraedts, V.J., Koch, M., Contarino, M.F., Middelkoop, H.A.M., Wang, H., van Hilten, J.J., Bäck, T.H.W., Tannemaat, M.R.

Publikováno v: In Clinical Neurophysiology May 2021 132(5):1041-1048

A systematic approach to analyze the computational cost of robustness in model-assisted robust optimization: a systematic approach to analyze the computational cost

Autor: Ullah, S., Wang, H., Menzel, S., Sendhoff, B., Bäck, T.H.W., Rudolph, G., Kononova, A.V., Aguirre, H., Kerschke, P., Ochoa, G., Tušar, T.

Publikováno v: Lecture notes in computer science (LNCS), 63-75. Cham: Springer
STARTPAGE=63;ENDPAGE=75;TITLE=Lecture notes in computer science (LNCS)

Real-world optimization scenarios under uncertainty and noise are typically handled with robust optimization techniques, which re-formulate the original optimization problem into a robust counterpart, e.g., by taking an average of the function values

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::d9dcbe1dcf8149c0a362d5f0a8db45f9
http://hdl.handle.net/1887/3463877

Multi-point acquisition function for constraint parallel efficient multi-objective optimization

Autor: Winter, R. de, Stein, B. van, Bäck, T.H.W., Fieldsend, E.J.

Publikováno v: GECCO '22: Proceedings of the genetic and evolutionary computation conference, 511-519. New York: Association for Computing Machinery
STARTPAGE=511;ENDPAGE=519;TITLE=GECCO '22: Proceedings of the genetic and evolutionary computation conference

Bayesian optimization is often used to optimize expensive black box optimization problems with long simulation times. Typically Bayesian optimization algorithms propose one solution per iteration. The downside of this strategy is the sub-optimal use

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::10f8a062c7b02789093ad1b9593e04ec
https://doi.org/10.1145/3512290.3528696

Quantum-Enhanced Selection Operators for Evolutionary Algorithms

Autor: Von Dollen, D., Yarkoni, S., Weimer, D., Neukart, F., Bäck, T.H.W., Fieldsend, J.E.

Publikováno v: GECCO '22: Proceedings of the genetic and evolutionary computation conference companion, 463-466. New York, U.S.A.: ACM
STARTPAGE=463;ENDPAGE=466;TITLE=GECCO '22: Proceedings of the genetic and evolutionary computation conference companion

Genetic algorithms have unique properties which are useful when applied to black-box optimization. Using selection, crossover, and mutation operators, candidate solutions may be obtained without the need to calculate a gradient. In this work, we stud

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8fc133a521a1456a57c96ed2968604f4
http://arxiv.org/abs/2206.10743

Unsupervised strategies for identifying optimal parameters in Quantum Approximate Optimization Algorithm

Autor: MOUSSA, C., Wang, H., Bäck T.H.W., Dunjko, V

Publikováno v: EPJ Quantum Technology, 9

As combinatorial optimization is one of the main quantum computing applications, many methods based on parameterized quantum circuits are being developed. In general, a set of parameters are being tweaked to optimize a cost function out of the quantu

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1f2f87043660830804f5314a722153ee
https://doi.org/10.1140/epjqt/s40507-022-00131-4