Generalized offline orthant search: one code for many problems in multiobjective optimization.

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

Crynodeb

We introduce generalized offline orthant search, an algorithmic framework that can be used to solve many problems coming from evolutionary multiobjective optimization using a common well-optimized algorithmic core and relatively cheap reduction procedures. The complexity of the core procedure is O(n · (log n)k-1) for n points of dimension k, and it has a good performance in practice.

We show that the presented approach can perform various flavors of non-dominated sorting, dominance counting, evaluate the ε-indicator and perform initial fitness assignment for IBEA. It is either competitive with the state-of-the-art algorithms or completely outperforms them for higher problem sizes. We hope that this approach will simplify future development of efficient algorithms for evolutionary multiobjective optimization.
Iaith wreiddiolSaesneg
TeitlGECCO '18
Is-deitlProceedings of the Genetic and Evolutionary Computation Conference
GolygyddionHernan Aguirre, Keiki Takadama
CyhoeddwrAssociation for Computing Machinery, Inc
Tudalennau593-600
Nifer y tudalennau8
ISBN (Argraffiad)978-1-4503-5618-3
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 02 Gorff 2018
DigwyddiadGECCO 2018: The Genetic and Evolutionary Computation Conference - Kyoto, Siapan
Hyd: 15 Gorff 201819 Gorff 2018
http://gecco-2018.sigevo.org

Cynhadledd

CynhadleddGECCO 2018: The Genetic and Evolutionary Computation Conference
Gwlad/TiriogaethSiapan
DinasKyoto
Cyfnod15 Gorff 201819 Gorff 2018
Cyfeiriad rhyngrwyd

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