Počet záznamů: 1  

Case study: constraint handling in evolutionary optimization of catalytic materials

  1. 1.
    SYSNO ASEP0362974
    Druh ASEPC - Konferenční příspěvek (mezinárodní konf.)
    Zařazení RIVD - Článek ve sborníku
    NázevCase study: constraint handling in evolutionary optimization of catalytic materials
    Tvůrce(i) Holeňa, Martin (UIVT-O) SAI, RID
    Linke, D. (DE)
    Bajer, Lukáš (UIVT-O) SAI, RID, ORCID
    Zdroj.dok.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
    Rozsah strans. 333-339
    Poč.str.7 s.
    AkceGECCO 2011. Genetic and Evolutionary Computation Conference /13./
    Datum konání12.07.2011-16.07.2011
    Místo konáníDublin
    ZeměIE - Irsko
    Typ akceWRD
    Jazyk dok.eng - angličtina
    Země vyd.US - Spojené státy americké
    Klíč. slovaevolutionary optimization ; mixed optimization ; equality constraints ; inequality constraints ; cardinality constraints
    Vědní obor RIVIN - Informatika
    CEPGA201/08/0802 GA ČR - Grantová agentura ČR
    GAP202/11/1368 GA ČR - Grantová agentura ČR
    CEZAV0Z10300504 - UIVT-O (2005-2011)
    EID SCOPUS80051952288
    DOI10.1145/2001858.2002015
    AnotaceThe 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.
    PracovištěÚstav informatiky
    KontaktTereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800
    Rok sběru2012
Počet záznamů: 1  

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