Výsledky vyhledávání - "Stein, B. van"

Using structural bias to analyse the behaviour of modular CMA-ES

Autor: Vermetten, D.L., Caraffini, F., Stein, B. van, Kononova, A.V., Fieldsend, J.E.

Publikováno v: GECCO '22: Proceedings of the genetic and evolutionary computation conference companion, 1674-1682. Boston: ACM
STARTPAGE=1674;ENDPAGE=1682;TITLE=GECCO '22: Proceedings of the genetic and evolutionary computation conference companion

The Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is a commonly used iterative optimisation heuristic for optimising black-box functions. CMA-ES comes in many flavours with different configuration settings. In this work, we investigate whe

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::74a3df11cd511d7d7a9bb50398d37193
https://doi.org/10.1145/3520304.3534035

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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

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Learning the characteristics of engineering optimization problems with applications in automotive crash

Autor: Long, F.X., Stein, B. van, Frenzel, M., Krause, P., Gitterle, M., Bäck, T.H.W., Fieldsend, J.E.

Publikováno v: GECCO '22: Proceedings of the genetic and evolutionary computation conference, 1227-1236. ACM
STARTPAGE=1227;ENDPAGE=1236;TITLE=GECCO '22: Proceedings of the genetic and evolutionary computation conference

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::15fa477a2a9fbbf6e6751bc6bedb250a
https://doi.org/10.1145/3512290.3528712

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Optimally weighted ensembles for efficient multi-objective optimization

Autor: Hanse, G., Winter, R. de, Stein, B. van, Bäck, T.H.W., Nicosia, G., Ojha, V., Malfa, E. La, Malfa, G. La, Jansen, G., Pardalos, P.M., Giuffrida, G., Umeton, R.

Publikováno v: Machine Learning, Optimization, and Data Science. LOD 2021, 144-156. Cham: Springer
STARTPAGE=144;ENDPAGE=156;TITLE=Machine Learning, Optimization, and Data Science. LOD 2021

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::814ac04cc416d31740bd786e5847d31b
http://hdl.handle.net/1887/3279362

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Analysis of structural bias in Differential Evolution configurations

Autor: Vermetten, D.L., Stein, B. van, Kononova, A.V., Caraffini, F., Kumar, B.V., Oliva, D., Suganthan, P.N

Publikováno v: Studies in Computational Intelligence, 1-22. Singapore: Springer
STARTPAGE=1;ENDPAGE=22;TITLE=Studies in Computational Intelligence
Studies in Computational Intelligence ISBN: 9789811680816

Differential Evolution is a popular optimisation method with a small number of parameters. However, different hyper-parameters and Differential Evolution variants such as different mutation operators and the F and Cr parameter may introduce structura

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c42429d91104da44ffd7730d7e3256f5
https://doi.org/10.1007/978-981-16-8082-3_1

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Cluster-based Kriging approximation algorithms for complexity reduction

Autor: Stein, B. van, Wang, H., Kowalczyk, W.J., Emmerich, M.T.M., Bäck, T.H.W.

Publikováno v: Applied Intelligence

Kriging or Gaussian Process Regression is applied in many fields as a non-linear regression model as well as a surrogate model in the field of evolutionary computation. However, the computational and space complexity of Kriging, that is cubic and qua

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c1f738cf2bbe5a78e2f6d5f801fbe107
https://doi.org/10.1007/s10489-019-01549-7

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SAMO-COBRA: a fast surrogate assisted constrained multi-objective optimization algorithm

Autor: Winter, R. de, Stein, B. van, Bäck, T.H.W., Ishibuchi, H., Zhang, Q., Cheng, R., Li, K., Li, H., Wang, H., Zhou, A.

Publikováno v: Evolutionary Multi-Criterion Optimization, 270-282. Cham: Springer Nature Switzerland AG 2021
STARTPAGE=270;ENDPAGE=282;TITLE=Evolutionary Multi-Criterion Optimization
Lecture Notes in Computer Science ISBN: 9783030720612
EMO

This paper proposes a novel Self-Adaptive algorithm for Multi-Objective Constrained Optimization by using Radial Basis Function Approximations, SAMO-COBRA. The algorithm automatically determines the best Radial Basis Function-fit as surrogates for th

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5565f8e6b98496a37964feea12b69a7e
https://doi.org/10.1007/978-3-030-72062-9_22

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Using machine learning to detect rotational and local reflectional symmetries in 2D images

Autor: Ponse, K., Kononova, A.V., Loleyt, M., Stein, B. van

Publikováno v: 2021 IEEE Symposium Series on Computational Intelligence (SSCI) Proceedings, 01-08. Orlando, Florida: IEEE
STARTPAGE=01;ENDPAGE=08;TITLE=2021 IEEE Symposium Series on Computational Intelligence (SSCI) Proceedings

Automated symmetry detection is still a difficult task in 2021. However, it has applications in computer vision, and it also plays an important part in understanding art. This paper focuses on aiding the latter by comparing different state-of-the-art

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::aaec2b72521ea4e1d7cc89634186c358
https://ieeexplore.ieee.org/document/9660120

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Ship design performance and cost optimization with machine learning

Autor: Winter, R. de, Stein, B. van, Bäck, T.H.W., Bertram, V.

Publikováno v: COMPIT'21, 185-196. Mülheim: Hamburg University of Technology
STARTPAGE=185;ENDPAGE=196;TITLE=COMPIT'21

This contribution shows how, in the preliminary design stage, naval architects can make more informed decisions by using machine learning. In this ship design phase, little information is available, and decisions need to be made in a limited amount o

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::11c0269ffdeb5a9f27d08adc310a1310
https://hdl.handle.net/1887/3245497

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Emergence of Structural Bias in Differential Evolution

Autor: Stein, B. van, Caraffini, F., Kononova, A.V., Chicano, F.

Publikováno v: GECCO Companion
Proceedings of the Genetic and Evolutionary Computation Conference Companion, 1234-1242. ACM
STARTPAGE=1234;ENDPAGE=1242;TITLE=Proceedings of the Genetic and Evolutionary Computation Conference Companion

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link. Heuristic optimisation algorithms are in high demand due to the overwhelming amount of complex optimis

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