A Comparison of Global Sensitivity Analysis Methods for Explainable AI With an Application in Genomic Prediction

Autor: Bas Van Stein, Elena Raponi, Zahra Sadeghi, Niek Bouman, Roeland C. H. J. Van Ham, Thomas Back

Publikováno v: IEEE Access, Vol 10, Pp 103364-103381 (2022)

Explainable Artificial Intelligence (XAI) is an increasingly important field of research required to bring AI to the next level in real-world applications. Global sensitivity analysis (GSA) methods play an important role in XAI, as they can provide a

Externí odkaz: https://doaj.org/article/912602b853f048eea9e8c199e54528ad

BBOB Instance Analysis: Landscape Properties and Algorithm Performance Across Problem Instances

Autor: Fu Xing Long, Diederick Vermetten, Bas van Stein, Anna V. Kononova

Publikováno v: Applications of Evolutionary Computation ISBN: 9783031302282

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::fceff5c09b4cb7a0ca7e23f443431d9c
https://doi.org/10.1007/978-3-031-30229-9_25

Constrained multi-objective optimization with a limited budget of function evaluations

Autor: Roy de Winter, Philip Bronkhorst, Bas van Stein, Thomas Bäck

Publikováno v: Memetic Computing, 14, 151-174. Springer Science and Business Media {LLC}

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

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3be504e3002603eedcc819613f563b1d
https://hdl.handle.net/1887/3303466

Explainable artificial intelligence for exhaust gas temperature of turbofan engines

Autor: Marios Kefalas, Juan de Santiago Rojo, Asteris Apostolidis, Dirk van den Herik, Bas van Stein, Thomas Bäck

Publikováno v: Journal of Aerospace Information Systems. American Institute of Aeronautics and Astronautics (AIAA)
Journal of Aerospace Information Systems
Journal of Aerospace Information Systems, 19(6), 447-454. American Institute of Aeronautics and Astronautics

Data-driven modeling is an imperative tool in various industrial applications, including many applications in the sectors of aeronautics and commercial aviation. These models are in charge of providing key insights, such as which parameters are impor

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b591cfa3c80265225f0d8648f616fd43
http://hdl.handle.net/1887/3281132

Multi-task shape optimization using a 3D point cloud autoencoder as unified representation

Autor: Thiago Rios, Bernhard Sendhoff, Bas van Stein, Stefan Menzel, Thomas Bäck

Publikováno v: IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation, 26(2), 206-217. Institute of Electrical and Electronics Engineers (IEEE)

The choice of design representations, as of search operators, is central to the performance of evolutionary optimization algorithms, in particular for multi-task problems. The multi-task approach pushes further the parallelization aspect of these alg

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::80b1ea91961e05bc9b810909e328c51e
https://doi.org/10.1109/tevc.2021.3086308