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Meta-Learning and Model Selection in Multiobjective Evolutionary Algorithms
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SYSNO ASEP 0384809 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Meta-Learning and Model Selection in Multiobjective Evolutionary Algorithms Author(s) Pilát, M. (CZ)
Neruda, Roman (UIVT-O) SAI, RID, ORCIDSource Title Proceedings 2012 11th International Conference on Machine Learning and Applications ICMLA 2012. - Los Alamitos : IEEE Computer Society, 2012 / Wani M.A. ; Khoshgoftaar T. ; Zhu X. ; Seliya N. - ISBN 978-1-4673-4651-1 Pages s. 433-438 Number of pages 6 s. Publication form Print - P Action ICMLA 2012. International Conference on Machine Learning and Applications /11./ Event date 12.12.2012-15.12.2012 VEvent location Boca Raton Country US - United States Event type WRD Language eng - English Country US - United States Keywords multiobjective optimization ; surrogate modelling ; meta-learning ; model selection Subject RIV IN - Informatics, Computer Science R&D Projects GAP202/11/1368 GA ČR - Czech Science Foundation (CSF) GD201/09/H057 GA ČR - Czech Science Foundation (CSF) Institutional support UIVT-O - RVO:67985807 UT WOS 000427260500072 EID SCOPUS 84873596265 DOI https://doi.org/10.1109/ICMLA.2012.78 Annotation Most existing surrogate based evolutionary algorithms deal with only one model selected by the authors and different models are not considered. In this paper we propose a framework which enables automatic selection of types of surrogate models, and evaluate the effect of the type of selection on the overall performance of the resulting evolutionary algorithm. Two different types of model selection are tested and compared both in pre-selection scenario and in local search scenario. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2013
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