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dc.creatorFerreira Neto,José Ambrósio
dc.creatorCarneiro dos Santos Junior,Edgard
dc.creatorFra Paleo,Urbano
dc.creatorMiranda Barros,David
dc.creatorCésar de Oliveira Moreira,Mayron
dc.date2011-08-01
dc.date.accessioned2019-05-17T13:11:31Z
dc.date.available2019-05-17T13:11:31Z
dc.identifierhttps://scielo.conicyt.cl/scielo.php?script=sci_arttext&pid=S0718-16202011000200001
dc.identifier.urihttps://revistaschilenas.uchile.cl/handle/2250/97992
dc.descriptionThe objective of this manuscript is to develop a new procedure to achieve optimal land subdivision using genetic algorithms (GA). The genetic algorithm was tested in the rural settlement of Veredas, located in Minas Gerais, Brazil. This implementation was based on the land aptitude and its productivity index. The sequence of tests in the study was carried out in two areas with eight different agricultural aptitude classes, including one area of 391.88 ha subdivided into 12 lots and another of 404.1763 ha subdivided into 14 lots. The effectiveness of the method was measured using the shunting line standard value of a parceled area lot's productivity index. To evaluate each parameter, a sequence of 15 calculations was performed to record the best individual fitness average (MMI) found for each parameter variation. The best parameter combination found in testing and used to generate the new parceling with the GA was the following: 320 as the generation number, a population of 40 individuals, 0.8 mutation tax, and a 0.3 renewal tax. The solution generated rather homogeneous lots in terms of productive capacity.
dc.formattext/html
dc.languageen
dc.publisherPontificia Universidad Católica de Chile. Facultad de Agronomía e Ingeniería Forestal
dc.relation10.4067/S0718-16202011000200001
dc.rightsinfo:eu-repo/semantics/openAccess
dc.sourceCiencia e investigación agraria v.38 n.2 2011
dc.subjectAgrarian reform
dc.subjectgenetic algorithm
dc.subjectrural settlement
dc.subjectspatial planning
dc.titleOptimal subdivision of land in agrarian reform projects: an analysis using genetic algorithms


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