Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/61475
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dc.contributor.authorWeise, Thomasen
dc.contributor.authorChiong, Raymonden
dc.date.accessioned2024-07-10T01:06:46Z-
dc.date.available2024-07-10T01:06:46Z-
dc.date.issued2015-01-05-
dc.identifier.citationNeurocomputing, 147(1), p. 235-238en
dc.identifier.issn1872-8286en
dc.identifier.issn0925-2312en
dc.identifier.urihttps://hdl.handle.net/1959.11/61475-
dc.description.abstract<p>Stochastic approaches such as evolutionary algorithms have been widely used in various science and engineering problems. When comparing the performance of a set of stochastic algorithms, it is necessary to statistically evaluate which algorithms are the most suitable for solving a given problem. The outcome of statistical tests comparing <i>N</i> ≥ 2 processes, where N is the number of algorithms, is often presented in tables. This can become confusing for larger numbers of <i>N</i>. Such a scenario is, however, very common in both numerical and combinatorial optimization as well as in the domain of stochastic algorithms in general. In this letter, we introduce an alternative way of visually presenting the results of statistical tests for multiple processes in a compact and easy-to-read manner using a directed acyclic graph (DAG), in the form of a simplified Hasse diagram. The rationale of doing so is based on the fact that the outcome of the tests is always at least a strict partial order, which can be appropriately presented via a DAG. The goal of this brief communication is to promote the use of this approach as a means for presenting the results of comparisons between different optimization methods.</p>en
dc.languageenen
dc.publisherElsevier BVen
dc.relation.ispartofNeurocomputingen
dc.titleAn alternative way of presenting statistical test results when evaluating the performance of stochastic approachesen
dc.typeJournal Articleen
dc.identifier.doi10.1016/j.neucom.2014.06.071en
local.contributor.firstnameThomasen
local.contributor.firstnameRaymonden
local.profile.schoolSchool of Science & Technologyen
local.profile.emailrchiong@une.edu.auen
local.output.categoryC1en
local.record.placeauen
local.record.institutionUniversity of New Englanden
local.publisher.placeThe Netherlandsen
local.format.startpage235en
local.format.endpage238en
local.peerreviewedYesen
local.identifier.volume147en
local.identifier.issue1en
local.contributor.lastnameWeiseen
local.contributor.lastnameChiongen
dc.identifier.staffune-id:rchiongen
local.profile.orcid0000-0002-8285-1903en
local.profile.roleauthoren
local.profile.roleauthoren
local.identifier.unepublicationidune:1959.11/61475en
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
local.title.maintitleAn alternative way of presenting statistical test results when evaluating the performance of stochastic approachesen
local.output.categorydescriptionC1 Refereed Article in a Scholarly Journalen
local.search.authorWeise, Thomasen
local.search.authorChiong, Raymonden
local.open.fileurlhttps://rune.une.edu.au/web/retrieve/9e1a7663-f78a-43fd-9b04-b4a628e84758en
local.uneassociationNoen
local.atsiresearchNoen
local.sensitive.culturalNoen
local.year.published2015en
local.fileurl.openhttps://rune.une.edu.au/web/retrieve/9e1a7663-f78a-43fd-9b04-b4a628e84758en
local.fileurl.closedpublishedhttps://rune.une.edu.au/web/retrieve/9e1a7663-f78a-43fd-9b04-b4a628e84758en
local.subject.for20204602 Artificial intelligenceen
local.profile.affiliationtypeExternal Affiliationen
local.profile.affiliationtypeExternal Affiliationen
local.date.moved2024-08-23en
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School of Science and Technology
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