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dc.contributor.authorLiefooghe, A
dc.contributor.authorVerel, S
dc.contributor.authorChugh, T
dc.contributor.authorFieldsend, J
dc.contributor.authorAllmendinger, R
dc.contributor.authorMiettinen, K
dc.date.accessioned2023-04-14T13:33:24Z
dc.date.issued2023-03-09
dc.date.updated2023-04-14T13:03:05Z
dc.description.abstractWe consider the application of machine learning techniques to gain insights into the effect of problem features on algorithm performance, and to automate the task of algorithm selection for distancebased multi- and many-objective optimisation problems. This is the most extensive benchmark study of such problems to date. The problem features can be set directly by the problem generator, and include e.g. the number of variables, objectives, local fronts, and disconnected Pareto sets. Using 945 problem configurations (leading to 28 350 instances) of varying complexity, we find that the problem features and the available optimisation budget (i) affect the considered algorithms (NSGA-II, IBEA, MOEA/D, and random search) in different ways and that (ii) it is possible to recommend a relevant algorithm based on problem features.en_GB
dc.format.extent260-273
dc.identifier.citationIn: Evolutionary Multi-Criterion Optimization. EMO 2023. Lecture Notes in Computer Science, vol. 13970, pp. 260-273en_GB
dc.identifier.doihttps://doi.org/10.1007/978-3-031-27250-9_19
dc.identifier.urihttp://hdl.handle.net/10871/132908
dc.identifierORCID: 0000-0001-5123-8148 (Chugh, Tinkle)
dc.identifierORCID: 0000-0002-0683-2583 (Fieldsend, Jonathan)
dc.language.isoenen_GB
dc.publisherSpringeren_GB
dc.relation.urlhttps://github.com/tichugh/Feature_Analysis_DBMOPP_EMO_2023en_GB
dc.relation.urlhttps://doi.org/10.5281/zenodo.7155803en_GB
dc.rights.embargoreasonUnder embargo until 9 March 2024 in compliance with publisher policyen_GB
dc.rights© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AGen_GB
dc.subjectMulti/many-objective distance problemsen_GB
dc.subjectFeature-based performance predictionen_GB
dc.subjectAutomated algorithm selectionen_GB
dc.titleFeature-based benchmarking of distance-based multi/many-objective optimisation problems: A machine learning perspectiveen_GB
dc.typeBook chapteren_GB
dc.date.available2023-04-14T13:33:24Z
dc.identifier.isbn9783031272493
dc.descriptionThis is the author accepted manuscript. The final version is available from Springer via the DOI in this recorden_GB
dc.descriptionThe code is available at: https://github.com/ tichugh/Feature_Analysis_DBMOPP_EMO_2023, and the corresponding dataset at: https://doi.org/10.5281/zenodo.7155803.en_GB
dc.rights.urihttp://www.rioxx.net/licenses/all-rights-reserveden_GB
dcterms.dateAccepted2023
rioxxterms.versionAMen_GB
rioxxterms.licenseref.startdate2023-03-09
rioxxterms.typeBook chapteren_GB
refterms.dateFCD2023-04-14T13:24:30Z
refterms.versionFCDAM
refterms.dateFirstOnline2023-03-09


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