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Fractional dynamics of genetic algorithms using hexagonal space tessellation

dc.contributor.authorMachado, J. A. Tenreiro
dc.date.accessioned2014-01-23T14:38:52Z
dc.date.available2014-01-23T14:38:52Z
dc.date.issued2013
dc.description.abstractThe paper formulates a genetic algorithm that evolves two types of objects in a plane. The fitness function promotes a relationship between the objects that is optimal when some kind of interface between them occurs. Furthermore, the algorithm adopts an hexagonal tessellation of the two-dimensional space for promoting an efficient method of the neighbour modelling. The genetic algorithm produces special patterns with resemblances to those revealed in percolation phenomena or in the symbiosis found in lichens. Besides the analysis of the spacial layout, a modelling of the time evolution is performed by adopting a distance measure and the modelling in the Fourier domain in the perspective of fractional calculus. The results reveal a consistent, and easy to interpret, set of model parameters for distinct operating conditions.por
dc.identifier.eid10.1155/2013/739464pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.22/3447
dc.language.isoengpor
dc.peerreviewedyespor
dc.publisherHindawi Publishing Corporationpor
dc.relation.ispartofseriesAbstract and Applied Analysis; Vol. 2013
dc.relation.publisherversionhttp://www.hindawi.com/journals/aaa/2013/739464/por
dc.titleFractional dynamics of genetic algorithms using hexagonal space tessellationpor
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage7por
oaire.citation.startPage1por
oaire.citation.titleAbstract and Applied Analysispor
oaire.citation.volumeVol. 2013por
person.familyNameTenreiro Machado
person.givenNameJ. A.
person.identifier.ciencia-id7A18-4935-5B29
person.identifier.orcid0000-0003-4274-4879
person.identifier.ridM-2173-2013
person.identifier.scopus-author-id55989030100
rcaap.rightsopenAccesspor
rcaap.typearticlepor
relation.isAuthorOfPublication82cd5c17-07b6-492b-b3e3-ecebdad1254f
relation.isAuthorOfPublication.latestForDiscovery82cd5c17-07b6-492b-b3e3-ecebdad1254f

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