Building Multivariate Densities for Simulating Samples in Promising Search Regions

dc.creatorSegovia Domínguez, Ignacio de Jesús
dc.date2016
dc.date.accessioned2018-11-19T14:18:46Z
dc.date.available2018-11-19T14:18:46Z
dc.identifierhttp://cimat.repositorioinstitucional.mx/jspui/handle/1008/491
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/2257198
dc.descriptionNowadays, many proposals for searching the optimum vector of a continuous optimisation problem are based on the stochastic perspective. These methods are successful due to the fact that random components add robustness to the premature convergence to a
dc.formatapplication/pdf
dc.languageeng
dc.publisherCIMAT
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://creativecommons.org/about/cc0/
dc.subjectinfo:eu-repo/classification/MSC/Optimisation, Evolutionary Computation, Gradient Estimates, Multivariate Statistical Density, Promising Vector, Estimation of Distribution Algorithm, Continuous Optimisation Problem, Information Geometry, Predictive Density Function, Promising Search Regi
dc.subjectinfo:eu-repo/classification/cti/1
dc.titleBuilding Multivariate Densities for Simulating Samples in Promising Search Regions
dc.typeTesis
dc.audiencestudents


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