Case study: constraint handling in evolutionary optimization of catalytic materials

Kód výsledku v IS VaVaI

RIV/67985807:_____/11:00362974 - isvavai.cz

Výsledek na webu

http://dx.doi.org/10.1145/2001858.2002015

DOI - Digital Object Identifier

10.1145/2001858.2002015

Jazyk výsledku

angličtina

Název v původním jazyce

Case study: constraint handling in evolutionary optimization of catalytic materials

Popis výsledku v původním jazyce

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

Název v anglickém jazyce

Case study: constraint handling in evolutionary optimization of catalytic materials

Popis výsledku anglicky

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

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2011

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

GECCO '11 Proceedings of the 13th annual conference companion on Genetic and evolutionary computation

ISBN

978-1-4503-0690-4

ISSN

—

e-ISSN

—

Počet stran výsledku

7

Strana od-do

333-339

Název nakladatele

ACM

Místo vydání

New York

Místo konání akce

DUBLIN

Datum konání akce

12. 7. 2011

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—