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dc.contributor.authorHernández López, Montserrat 
dc.contributor.authorCáceres Hernández, José Juan
dc.date.accessioned2024-12-23T11:34:02Z
dc.date.available2024-12-23T11:34:02Z
dc.date.issued2016
dc.identifier.urihttp://riull.ull.es/xmlui/handle/915/40638
dc.description.abstractA genetic algorithm is developed to forecast the relative presence of different university studies in the higher education demand in the field of economics and business/management as a whole. A selection operator is defined that assumes that the better the job opportunities associated with a specific university study, the higher the future demand for such a degree. A transition matrix takes other factors into account which may influence on the changes in demand. The proposed algorithm is applied to the original populations of students enrolled on 2005/2006 to 2007/2008 courses. Then, a new algorithm, whose elements are corrected to adjust the forecasts, is applied to obtain the forecast of the demand composition on the 2009/2010 course. This methodological proposal is shown to be able to provide the type of forecast which is very useful in policy making decisions in the recent process of building the European Higher Education Area.es_ES
dc.language.isoenes_ES
dc.relation.ispartofseriesEconomic Computation and Economic Cybernetics Studies and Research, Issue 3/2016, Vol. 50;
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.titleForecasting the composition of demand for higher education degrees by genetic algorithmses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.subject.keywordhigher educationes_ES
dc.subject.keywordforecastinges_ES
dc.subject.keywordgenetic algorithmses_ES
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones_ES


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