Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/14377
Full metadata record
DC FieldValueLanguage
dc.contributor.authorJANSSENS, Gerrit-
dc.contributor.authorPangilinan, Jose Maria-
dc.date.accessioned2012-11-16T11:26:37Z-
dc.date.available2012-11-16T11:26:37Z-
dc.date.issued2010-
dc.identifier.citationPapadopoulos, H Andreou, AS Bramer, M (Ed.). ARTIFICIAL INTELLIGENCE APPLICATIONS AND INNOVATIONS, p. 94-103-
dc.identifier.isbn978-3-642-16238-1-
dc.identifier.issn1868-4238-
dc.identifier.urihttp://hdl.handle.net/1942/14377-
dc.description.abstractThe paper shows the importance of a multi-criteria performance analysis in evaluating the quality of non-dominated sets. The sets are generated by the use of evolutionary algorithms, more specifically through SPEA2 or NSGA-II. Problem examples from different problem domains are analyzed on four criteria of quality. These four criteria namely cardinality of the non-dominated set, spread of the solutions, hyper-volume, and set coverage do not favour any algorithm along the problem examples. In the Multiple Shortest Path Problem (MSPP) examples, the spread of solutions is the decisive factor for the 2SI1M configuration, and the carchnality and set coverage for the 3S configuration. The differences in set coverage values between SPEA2 and NSGA-II in the MSPP are small since both algorithms have almost identical non-dominated solutions. In the Decision Tree examples, the decisive factors are set coverage and hyper-volume. The computations show that the decisive criterion or criteria vary in all examples except for the set coverage criterion. This shows the importance of a binary measure in evaluating the quality of non-dominated sets, as the measure itself tests for dominance. The various criteria are confronted by means of a multi-criteria decision tool.-
dc.language.isoen-
dc.publisherSPRINGER-VERLAG BERLIN-
dc.relation.ispartofseriesIFIP Advances in Information and Communication Technology-
dc.subject.otherComputer Science, Artificial Intelligence; Computer Science, Information Systems-
dc.subject.otherevolutionary algorithms; multi-objective optimization; multi-criteria; analysis-
dc.titleMultiple Criteria Performance Analysis of Non-dominated Sets Obtained by Multi-objective Evolutionary Algorithms for Optimisation-
dc.typeProceedings Paper-
local.bibliographicCitation.authorsPapadopoulos, H Andreou, AS Bramer, M-
local.bibliographicCitation.conferencedateOCT 06-07, 2010-
local.bibliographicCitation.conferencename6th IFIP Conference on Artificial Intelligence Applications and Innovations-
local.bibliographicCitation.conferenceplaceLarnaca, CYPRUS-
dc.identifier.epage103-
dc.identifier.spage94-
dc.identifier.volume339-
local.format.pages10-
local.bibliographicCitation.jcatC1-
dc.description.notes[Janssens, Gerrit K.] Hasselt Univ, Transportat Res Inst IMOB, B-3590 Diepenbeek, Belgium. gerrit.janssens@uhasselt.be; joey.pangilinan@slu.edu.ph-
local.publisher.placeBERLIN-
local.type.refereedRefereed-
local.type.specifiedProceedings Paper-
dc.bibliographicCitation.oldjcatC1-
dc.identifier.isi000293683700015-
local.bibliographicCitation.btitleARTIFICIAL INTELLIGENCE APPLICATIONS AND INNOVATIONS-
item.contributorJANSSENS, Gerrit-
item.contributorPangilinan, Jose Maria-
item.validationecoom 2012-
item.fulltextNo Fulltext-
item.fullcitationJANSSENS, Gerrit & Pangilinan, Jose Maria (2010) Multiple Criteria Performance Analysis of Non-dominated Sets Obtained by Multi-objective Evolutionary Algorithms for Optimisation. In: Papadopoulos, H Andreou, AS Bramer, M (Ed.). ARTIFICIAL INTELLIGENCE APPLICATIONS AND INNOVATIONS, p. 94-103.-
item.accessRightsClosed Access-
Appears in Collections:Research publications
Show simple item record

WEB OF SCIENCETM
Citations

7
checked on Jun 23, 2022

Page view(s)

44
checked on Jun 28, 2022

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.