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Evolutionary Optimization of Catalysts Assisted by Neural-Network Learning
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SYSNO ASEP 0351863 Druh ASEP C - Konferenční příspěvek (mezinárodní konf.) Zařazení RIV D - Článek ve sborníku Název Evolutionary Optimization of Catalysts Assisted by Neural-Network Learning Tvůrce(i) Holeňa, Martin (UIVT-O) SAI, RID
Linke, D. (DE)
Rodemerck, U. (DE)Zdroj.dok. 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 Rozsah stran s. 220-229 Poč.str. 10 s. Akce SEAL 2010. International Conference /8./ Datum konání 01.12.2010-04.12.2010 Místo konání Kanpur Země IN - Indie Typ akce WRD Jazyk dok. eng - angličtina Země vyd. DE - Německo Klíč. slova evolutionary optimization ; mixed optimization ; constrained optimization ; neural network learning ; surrogate modelling ; evolutionary algorithms in catalysis Vědní obor RIV IN - Informatika CEP GA201/08/0802 GA ČR - Grantová agentura ČR GEICC/08/E018 GA ČR - Grantová agentura ČR CEZ AV0Z10300504 - UIVT-O (2005-2011) UT WOS 000289185200023 EID SCOPUS 78650751185 DOI 10.1007/978-3-642-17298-4_23 Anotace 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. Pracoviště Ústav informatiky Kontakt Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Rok sběru 2011
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