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Case study: constraint handling in evolutionary optimization of catalytic materials
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SYSNO ASEP 0362974 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Case study: constraint handling in evolutionary optimization of catalytic materials Author(s) Holeňa, Martin (UIVT-O) SAI, RID
Linke, D. (DE)
Bajer, Lukáš (UIVT-O) SAI, RID, ORCIDSource Title GECCO '11 Proceedings of the 13th annual conference companion on Genetic and evolutionary computation. - New York : ACM, 2011 / Krasnogor N. - ISBN 978-1-4503-0690-4 Pages s. 333-339 Number of pages 7 s. Action GECCO 2011. Genetic and Evolutionary Computation Conference /13./ Event date 12.07.2011-16.07.2011 VEvent location Dublin Country IE - Ireland Event type WRD Language eng - English Country US - United States Keywords evolutionary optimization ; mixed optimization ; equality constraints ; inequality constraints ; cardinality constraints Subject RIV IN - Informatics, Computer Science R&D Projects GA201/08/0802 GA ČR - Czech Science Foundation (CSF) GAP202/11/1368 GA ČR - Czech Science Foundation (CSF) CEZ AV0Z10300504 - UIVT-O (2005-2011) EID SCOPUS 80051952288 DOI 10.1145/2001858.2002015 Annotation The paper presents a case study in an industrially important application domain the optimization of catalytic materials. Though evolutionary algorithms are the by far most frequent approach to optimization tasks in that domain, they are challenged by mixing continuous and discrete variables, and especially by a large number of constraints. The paper describes the various kinds of encountered constraints, and explains constraint handling in GENACAT, one of evolutionary optimization systems developed specifically for catalyst optimization. In particular, it is shown that the interplay between cardinality constraints and linear equality and inequality constraints allows GENACAT to efficienlty determine the set of feasible solutions, and to split the original optimization task into a sequence of discrete and continuous optimization. Finally, the genetic operations employed in the discrete optimization are sketched, among which crossover is based on an assumption about the importance of the choice of sets of continuous variables in the cardinality constraints. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2012
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