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3D fast convex-hull-based evolutionary multiobjective optimization algorithm

2018-06Journal article Open access
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dc.contributor.authorZhao, Jiaqi
dc.contributor.authorJiao, Licheng
dc.contributor.authorLiu, Fang
dc.contributor.authorBasto-Fernandes, Vitor
dc.contributor.authorYevseyeva, Iryna
dc.contributor.authorXia, Shixiong
dc.contributor.authorEmmerich, Michael T.M.
dc.date.accessioned2025-06-26T14:54:26Z
dc.date.available2025-06-26T14:54:26Z
dc.date.issued2018-06
dc.description.abstractThe receiver operating characteristic (ROC) and detection error tradeoff (DET) curves have been widely used in the machine learning community to analyze the performance of classifiers. The area (or volume) under the convex hull has been used as a scalar indicator for the performance of a set of classifiers in ROC and DET space. Recently, 3D convex-hull-based evolutionary multiobjective optimization algorithm (3DCH-EMOA) has been proposed to maximize the volume of convex hull for binary classification combined with parsimony and three-way classification problems. However, 3DCH-EMOA revealed high consumption of computational resources due to redundant convex hull calculations and a frequent execution of nondominated sorting. In this paper, we introduce incremental convex hull calculation and a fast replacement for non-dominated sorting. While achieving the same high quality results, the computational effort of 3DCH-EMOA can be reduced by orders of magnitude. The average time complexity of 3DCH-EMOA in each generation is reduced from to per iteration, where n is the population size. Six test function problems are used to test the performance of the newly proposed method, and the algorithms are compared to several state-of-the-art algorithms, including NSGA-III, RVEA, etc., which were not compared to 3DCH-EMOA before. Experimental results show that the new version of the algorithm (3DFCH-EMOA) can speed up 3DCH-EMOA for about 30 times for a typical population size of 300 without reducing the performance of the method. Besides, the proposed algorithm is applied for neural networks pruning, and several UCI datasets are used to test the performance.eng
dc.description.sponsorshipThis work was partially supported by the National Key Research and Development Plan (No. 2016YFC0600908), the National Natural Science Foundation of China (No. U1610124, 61572505 and 61772530), and the National Natural Science Foundation of Jiangsu Province (No. BK20171192). We also thank the authors of PlatEMO and LightNet.
dc.identifier.citationJiaqi Zhao, Licheng Jiao, Fang Liu, Vitor Basto Fernandes, Iryna Yevseyeva, Shixiong Xia, Michael T.M. Emmerich, 3D fast convex-hull-based evolutionary multiobjective optimization algorithm, Applied Soft Computing, Volume 67, 2018, Pages 322-336, ISSN 1568-4946, https://doi.org/10.1016/j.asoc.2018.03.005.
dc.identifier.doi10.1016/j.asoc.2018.03.005
dc.identifier.issn1568-4946
dc.identifier.urihttp://hdl.handle.net/10400.8/13428
dc.language.isoeng
dc.peerreviewedyes
dc.publisherElsevier BV
dc.relation.hasversionhttps://www.sciencedirect.com/science/article/pii/S1568494618301212
dc.relation.ispartofApplied Soft Computing
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectConvex hull
dc.subjectArea under ROC
dc.subjectIndicator-based evolutionary algorithm
dc.subjectMultiobjective optimization
dc.subjectROC analysis
dc.title3D fast convex-hull-based evolutionary multiobjective optimization algorithmeng
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage336
oaire.citation.startPage323
oaire.citation.titleApplied Soft Computing
oaire.citation.volume67
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameBasto-Fernandes
person.givenNameVitor
person.identifier.ciencia-id581C-52BB-AC4E
person.identifier.orcid0000-0003-4269-5114
person.identifier.ridN-1891-2016
person.identifier.scopus-author-id53363129900
relation.isAuthorOfPublicationfb2d3703-9d6a-4c22-bbc4-9ff14c162feb
relation.isAuthorOfPublication.latestForDiscoveryfb2d3703-9d6a-4c22-bbc4-9ff14c162feb

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