Various Degrees of Steadiness in NSGA-II and Their Influence on the Quality of Results

Maxim Buzdalov, Vladimir Parfenov

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion CynhadleddTrafodion Cynhadledd (Nid-Cyfnodolyn fathau)

Crynodeb

Steady-state evolutionary algorithms are often favoured over generational ones due to better scalability in parallel and distributed environments. However, in certain conditions they are able to produce results of better quality as well. We consider several ways to introduce various ``degrees of steadiness'' in the NSGA-II algorithm, some of which have not been known in literature, and show experimentally (on a corpus of 21 test problems) the presence of a general trend: algorithms with more steadiness yield better results.
Iaith wreiddiolSaesneg
TeitlGECCO Companion '15
Is-deitlProceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation
GolygyddionSara Silva
CyhoeddwrAssociation for Computing Machinery
Tudalennau749-750
Nifer y tudalennau2
ISBN (Argraffiad)978-1-4503-3488-4
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 11 Gorff 2015
Cyhoeddwyd yn allanolIe
Digwyddiad16th Genetic and Evolutionary Computation Conference, GECCO 2015 - Madrid, Sbaen
Hyd: 11 Gorff 201515 Gorff 2015

Cynhadledd

Cynhadledd16th Genetic and Evolutionary Computation Conference, GECCO 2015
Gwlad/TiriogaethSbaen
DinasMadrid
Cyfnod11 Gorff 201515 Gorff 2015

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