The effect of entropy on the performance of modified genetic algorithm using earthquake and wind time series

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dc.contributor.authorVargas, M.
dc.contributor.authorFuertes, G.
dc.contributor.authorAlfaro, M.
dc.contributor.authorGatica, G.
dc.contributor.authorGutierrez, S.
dc.contributor.authorPeralta, M.
dc.date.accessioned2021-12-03T15:30:10Z
dc.date.available2021-12-03T15:30:10Z
dc.date.issued2018-08
dc.descriptionIndexación: Scopuses
dc.description.abstractThe dynamic complexity of time series of natural phenomena allowed to improve the performance of the genetic algorithm to optimize the test mathematical functions. The initial populations of stochastic origin of the genetic algorithm were replaced using the series of time of winds and earthquakes. The determinism of the time series brings in more information in the search of the global optimum of the functions, achieving reductions of time and an improvement of the results. The information of the initial populations was measured using the entropy of Shannon and allowed to establish the importance of the entropy in the initial populations and its relation with getting better results. This research establishes a new methodology for using determinism time series to search the best performance of the models of optimization of genetic algorithms (GA). Copyright © 2018 Manuel Vargas et al. This is an open access article distributed under the Creative Commons Attribution Licensees
dc.description.urihttps://www.hindawi.com/journals/complexity/2018/4392036/
dc.identifier.citationComplexity Volume 2018 August 2018 Article number 4392036es
dc.identifier.doi10.1155/2018/4392036
dc.identifier.issn1076-2787
dc.identifier.urihttp://repositorio.unab.cl/xmlui/handle/ria/21166
dc.language.isoenes
dc.publisherHindawi Limitedes
dc.rights.licenseAtribución 4.0 Internacional (CC BY 4.0)
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/deed.es
dc.subjectGenetic algorithms.es
dc.subjectModified genetic algorithms.es
dc.subjectTime serieses
dc.titleThe effect of entropy on the performance of modified genetic algorithm using earthquake and wind time serieses
dc.typeArtículoes
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