Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/7735
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dc.contributor.authorLi, M-
dc.contributor.authorYang, S-
dc.contributor.authorZheng, J-
dc.contributor.authorLiu, X-
dc.date.accessioned2013-12-02T10:55:38Z-
dc.date.available2013-12-02T10:55:38Z-
dc.date.issued2013-
dc.identifier.citationEvolutionary Computation, 2013en_US
dc.identifier.issn1063-6560-
dc.identifier.urihttp://www.ncbi.nlm.nih.gov/pubmed/23746293en
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/7735-
dc.description© the Massachusetts Institute of Technologyen_US
dc.description.abstractAbstract The Euclidean minimum spanning tree (EMST), widely used in a variety of domains, is a minimum spanning tree of a set of points in the space, where the edge weight between each pair of points is their Euclidean distance. Since the generation of an EMST is entirely determined by the Euclidean distance between solutions (points), the properties of EMSTs have a close relation with the distribution and position information of solutions. This paper explores the properties of EMSTs and proposes an EMST-based Evolutionary Algorithm (ETEA) to solve multiobjective optimization problems (MOPs). Unlike most EMO algorithms that focus on the Pareto dominance relation, the proposed algorithm mainly considers distance-based measures to evaluate and compare individuals during the evolutionary search. Specifically in ETEA, four strategies are introduced: 1) An EMST-based crowding distance (ETCD) is presented to estimate the density of individuals in the population; 2) A distance comparison approach incorporating ETCD is used to assign the fitness value for individuals; 3) A fitness adjustment technique is designed to avoid the partial overcrowding in environmental selection; 4) Three diversity indicators-the minimum edge, degree, and ETCD-with regard to EMSTs are applied to determine the survival of individuals in archive truncation. From a series of extensive experiments on 32 test instances with different characteristics, ETEA is found to be competitive against five state-of-the-art algorithms and its predecessor in providing a good balance among convergence, uniformity, and spread.en_US
dc.description.sponsorshipEngineering and Physical Sciences Research Council (EPSRC) of the United Kingdom under Grant EP/K001310/1, and the National Natural Science Foundation of China under Grant 61070088.en_US
dc.languageENG-
dc.language.isoenen_US
dc.publisherMassachusetts Institute of Technology Pressen_US
dc.subjectMulti-objective optimizationen_US
dc.subjectEvolutionary algorithmsen_US
dc.subjectEvolutionary algorithms treeen_US
dc.subjectDensity estimationen_US
dc.subjectFitness assignmenten_US
dc.subjectFitness adjustmenten_US
dc.subjectArchive truncationen_US
dc.titleETEA: A euclidean minimum spanning tree-Based evolutionary algorithm for multiobjective optimizationen_US
dc.typeArticleen_US
dc.identifier.doihttp://dx.doi.org/10.1162/EVCO_a_00106-
pubs.organisational-data/Brunel-
pubs.organisational-data/Brunel/Brunel Active Staff-
pubs.organisational-data/Brunel/Brunel Active Staff/School of Info. Systems, Comp & Maths-
pubs.organisational-data/Brunel/Brunel Active Staff/School of Info. Systems, Comp & Maths/IS and Computing-
pubs.organisational-data/Brunel/University Research Centres and Groups-
pubs.organisational-data/Brunel/University Research Centres and Groups/School of Health Sciences and Social Care - URCs and Groups-
pubs.organisational-data/Brunel/University Research Centres and Groups/School of Health Sciences and Social Care - URCs and Groups/Centre for Systems and Synthetic Biology-
pubs.organisational-data/Brunel/University Research Centres and Groups/School of Information Systems, Computing and Mathematics - URCs and Groups-
pubs.organisational-data/Brunel/University Research Centres and Groups/School of Information Systems, Computing and Mathematics - URCs and Groups/Centre for Intelligent Data Analysis-
Appears in Collections:Publications
Computer Science
Dept of Computer Science Research Papers

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