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Surrogate Model for Mixed-Variables Evolutionary Optimization Based on GLM and RBF Networks
- 1.0389195 - ÚI 2013 RIV DE eng C - Conference Paper (international conference)
Bajer, Lukáš - Holeňa, Martin
Surrogate Model for Mixed-Variables Evolutionary Optimization Based on GLM and RBF Networks.
SOFSEM 2013. Theory and Practice of Computer Science. Berlin: Springer, 2013 - (van Emde Boas, P.; Groen, F.; Italiano, G.; Nawrocki, J.; Sack, H.), s. 481-490. Lecture Notes in Computer Science, 7741. ISBN 978-3-642-35842-5. ISSN 0302-9743.
[SOFSEM 2013. Conference on Current Trends in Theory and Practice of Computer Science /39./. Špindlerův Mlýn (CZ), 26.01.2013-31.01.2013]
R&D Projects: GA ČR GAP202/11/1368; GA ČR GA201/08/0802
Grant - others:GA UK(CZ) 278511/2011
Institutional support: RVO:67985807
Keywords : surrogate modelling * RBF networks * genetic algorithms * mixed-variables optimization * continuous and discrete variables
Subject RIV: IN - Informatics, Computer Science
Cited: 2
--- BAERT, L. - BEAUTHIER, C. - LEBORGNE, M. - LEPOT, I. SURROGATE-BASED OPTIMISATION FOR A MIXED-VARIABLE DESIGN SPACE: PROOF OF CONCEPT AND OPPORTUNITIES FOR TURBOMACHINERY APPLICATIONS. ASME TURBO EXPO: TURBINE TECHNICAL CONFERENCE AND EXPOSITION, 2015, VOL 2C. 2015. [WOS]
--- PELAMATTI, J. - BREVAULT, L. - BALESDENT, M. - TALBI, E. - GUERIN, Y. Efficient global optimization of constrained mixed variable problems. JOURNAL OF GLOBAL OPTIMIZATION. ISSN 0925-5001, MAR 2019, vol. 73, no. 3, p. 583-613. [WOS]
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