GOMEA: A Conceptual Design of a Membrane Electrode Assembly for a Proton Exchange Membrane Electrolyzer

Autor: Torsten Berning, Dmitri Bessarabov

Publikováno v: Membranes, Vol 13, Iss 7, p 614 (2023)

We are proposing a conceptual membrane electrode assembly (MEA) of a proton exchange membrane water electrolyzer that includes a layer of graphene oxide (GO) at the cathode side. This GO layer primarily reinforces the MEA to allow operation at a high

Externí odkaz: https://doaj.org/article/097ac58fa2a04613b207f4e5d6677d7b

GPU-accelerated parallel Gene-pool Optimal Mixing applied to multi-objective Deformable Image Registration

Autor: Anton Bouter, Peter A. N. Bosman, Tanja Alderliesten

Publikováno v: CEC

The Real-Valued Gene-pool Optimal Mixing Evolutionary Algorithm (RV-GOMEA) has previously been successfully used to achieve highly scalable optimization of various real-world problems in a gray-box optimization setting. Deformable Image Registration

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::78ef23a958e3a82b9b9fcd3e2fae9a84
https://doi.org/10.1109/cec45853.2021.9504840

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Leveraging conditional linkage models in gray-box optimization with the real-valued gene-pool optimal mixing evolutionary algorithm

Autor: Anton Bouter, Tanja Alderliesten, S. C. Maree, Peter A. N. Bosman

Publikováno v: GECCO
GECCO 2020-Proceedings of the 2020 Genetic and Evolutionary Computation Conference, 603-611
STARTPAGE=603;ENDPAGE=611;TITLE=GECCO 2020-Proceedings of the 2020 Genetic and Evolutionary Computation Conference
GECCO 2020: Proceedings of the 2020 Genetic and Evolutionary Computation Conference
GECCO 2020

Often, real-world problems are of the gray-box type. It has been shown that the Real-Valued Gene-pool Optimal Mixing Evolutionary Algorithm (RV-GOMEA) is in principle capable of exploiting such a Gray-Box Optimization (GBO) setting using linkage mode

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::91a65de9674a66bd087f374e1703d006
https://doi.org/10.1145/3377930.3390225

Improving Model-Based Genetic Programming for Symbolic Regression of Small Expressions

Autor: Tanja Alderliesten, Peter A. N. Bosman, Cees Witteveen, Marco Virgolin

Publikováno v: Evolutionary Computation, 29(2), 211-237. MIT PRESS
Evolutionary Computation, 29(2), 211-237

The Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) is a model-based EA framework that has been shown to perform well in several domains, including Genetic Programming (GP). Differently from traditional EAs where variation acts blindly, GOMEA

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9ee87b512690a28b42df05b8368aa4ca
https://pubmed.ncbi.nlm.nih.gov/32574084