Neural Networks as Surrogate Models for Measurements in Optimization Algorithms

0345993 - ÚI 2011 RIV DE eng C - Konferenční příspěvek (zahraniční konf.)
Holeňa, Martin - Linke, D. - Rodemerck, U. - Bajer, Lukáš
Neural Networks as Surrogate Models for Measurements in Optimization Algorithms.
Analytical and Stochastic Modeling Techniques and Applications. Berlin: Springer, 2010 - (Al-Begain, K.; Fiems, D.; Knottenbelt, W.), s. 351-366. Lecture Notes in Computer Science, 6148. ISBN 978-3-642-13567-5. ISSN 0302-9743.
[ASMTA 2010. International Conference /17./. Cardiff (GB), 14.06.2010-16.06.2010]
Grant CEP: GA ČR GA201/08/0802
Výzkumný záměr: CEZ:AV0Z10300504
Klíčová slova: functions evaluated via measurements * evolutionary optimization * surrogate modelling * neural networks * boosting
Kód oboru RIV: IN - Informatika

Citováno: 3

--- PILAT, M. - NERUDA, R. Aggregate meta-models for evolutionary multiobjective and many-objective optimization. NEUROCOMPUTING. ISSN 0925-2312, SEP 20 2013, vol. 116, SI, p. 392-402. [WOS]
--- THEUNISSEN, R. - GJELSTRUP, P. Adaptive sampling in higher dimensions for point-wise experimental measurement techniques. MEASUREMENT SCIENCE AND TECHNOLOGY. ISSN 0957-0233, AUG 2018, vol. 29, no. 8. [WOS]
--- ELMELIGY, A. - MEHRANI, P. - THIBAULT, J. Artificial Neural Networks as Metamodels for the Multiobjective Optimization of Biobutanol Production. APPLIED SCIENCES-BASEL. ISSN 2076-3417, JUN 2018, vol. 8, no. 6. [WOS]

Trvalý link: http://hdl.handle.net/11104/0187137