Current studentsExisting staffContact us
 

Runtime Analyses of NSGA-III on Many-Objective Problems

Simple item page
dc.contributor.authorOpris, A.
dc.contributor.authorDang, D.-C.
dc.contributor.authorNeumann, F.
dc.contributor.authorSudholt, D.
dc.contributor.conferenceGenetic and Evolutionary Computation Conference (GECCO) (14 Jul 2024 - 18 Jul 2024 : Melbourne, Victoria Australia and Virtual Online)
dc.contributor.editorLi, X.
dc.contributor.editorHandl, J.
dc.date.issued2024
dc.description.abstractNSGA-II and NSGA-III are two of the most popular evolutionary multi-objective algorithms used in practice. While NSGA-II is used for few objectives such as 2 and 3, NSGA-III is designed to deal with a larger number of objectives. In a recent breakthrough, Wietheger and Doerr (IJCAI 2023) gave the first runtime analysis for NSGA-III on the 3-objective OneMinMax problem, showing that this stateof-the-art algorithm can be analyzed rigorously. We advance this new line of research by presenting the first runtime analyses of NSGA-III on the popular many-objective benchmark problems 𝑚-LOTZ, 𝑚-OMM, and 𝑚-COCZ, for an arbitrary constant number 𝑚 of objectives. Our analysis provides ways to set the important parameters of the algorithm: the number of reference points and the population size, so that a good performance can be guaranteed. We show how these parameters should be scaled with the problem dimension, the number of objectives and the fitness range. To our knowledge, these are the first runtime analyses for NSGA-III for more than 3 objectives.
dc.description.statementofresponsibilityAndre Opris, Duc-Cuong Dang, Frank Neumann, Dirk Sudholt
dc.identifier.citationProceedings of the Genetic and Evolutionary Computation Conference (GECCO, 2024), 2024 / Li, X., Handl, J. (ed./s), pp.1596-1604
dc.identifier.doi10.1145/3638529.3654218
dc.identifier.orcidNeumann, F. [0000-0002-2721-3618]
dc.identifier.urihttps://hdl.handle.net/2440/144356
dc.language.isoen
dc.publisherAssociation for Computing Machinery
dc.relation.granthttp://purl.org/au-research/grants/arc/FT200100536
dc.rights© 2024 Copyright held by the owner/author(s). This work is licensed under a Creative Commons Attribution-NonCommercial International 4.0 License.
dc.source.urihttps://doi.org/10.1145/3638529.3654218
dc.subjectRuntime analysis; evolutionary multiobjective optimization
dc.titleRuntime Analyses of NSGA-III on Many-Objective Problems
dc.typeConference paper
pubs.publication-statusPublished

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
hdl_144356.pdf
Size:
798.26 KB
Format:
Adobe Portable Document Format
Description:
Published version
Download

Collections

Research Outputs