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Evolutionary Optimization of Catalysts Assisted by Neural-Network Learning
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SYSNO ASEP 0351863 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Evolutionary Optimization of Catalysts Assisted by Neural-Network Learning Author(s) Holeňa, Martin (UIVT-O) SAI, RID
Linke, D. (DE)
Rodemerck, U. (DE)Source Title Simulated Evolution and Learning. - Berlin : Springer, 2010 / Deb K. ; Bhattacharya A. ; Chakraborti N. ; Chakroborty P. ; Das S. ; Dutta J. ; Gupta S.K. ; Jain A. ; Aggarwal V. ; Branke J. ; Louis S.J. ; Tan K.C. - ISSN 0302-9743 - ISBN 978-3-642-17297-7 Pages s. 220-229 Number of pages 10 s. Action SEAL 2010. International Conference /8./ Event date 01.12.2010-04.12.2010 VEvent location Kanpur Country IN - India Event type WRD Language eng - English Country DE - Germany Keywords evolutionary optimization ; mixed optimization ; constrained optimization ; neural network learning ; surrogate modelling ; evolutionary algorithms in catalysis Subject RIV IN - Informatics, Computer Science R&D Projects GA201/08/0802 GA ČR - Czech Science Foundation (CSF) GEICC/08/E018 GA ČR - Czech Science Foundation (CSF) CEZ AV0Z10300504 - UIVT-O (2005-2011) UT WOS 000289185200023 EID SCOPUS 78650751185 DOI 10.1007/978-3-642-17298-4_23 Annotation This paper presents an important real-world application of both evolutionary computation and learning, an application to the search for optimal catalytic materials. In this area, evolutionary and especially genetic algorithms are encountered most frequently. However, their application is far from any standard methodology, due to problems with mixed optimization and constraints. The paper describes how these difficulties are dealt with in the evolutionary optimization system GENACAT, recently developed for searching optimal catalysts. It also recalls that the costly evaluation of objective functions in this application area can be tackled through learning suitable regression models of those functions, called surrogate models. Ongoing integration of neural-networks-based surrogate modelling with GENACAT is illustrated on two brief examples. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2011
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