A mixed evolutionary-statistical analysis of an algorithm's complexity

dc.creatorCotta, C
dc.creatorMoscato, P
dc.date2003
dc.dateJAN
dc.date2014-11-13T11:51:25Z
dc.date2015-11-26T18:07:15Z
dc.date2014-11-13T11:51:25Z
dc.date2015-11-26T18:07:15Z
dc.date.accessioned2018-03-29T00:49:23Z
dc.date.available2018-03-29T00:49:23Z
dc.identifierApplied Mathematics Letters. Pergamon-elsevier Science Ltd, v. 16, n. 1, n. 41, n. 47, 2003.
dc.identifier0893-9659
dc.identifierWOS:000179408900007
dc.identifier10.1016/S0893-9659(02)00142-8
dc.identifierhttp://www.repositorio.unicamp.br/jspui/handle/REPOSIP/76070
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/76070
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/76070
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1293547
dc.descriptionA combination of evolutionary algorithms and statistical techniques is used to analyze the worst-case computational complexity of two sorting algorithms. It is shown that excellent bounds for these algorithms can be obtained using this approach; this fact raises interesting prospects for applying the approach to other problems and algorithms. Several guidelines for extending this work are included. (C) 2002 Elsevier Science Ltd. All rights reserved.
dc.description16
dc.description1
dc.description41
dc.description47
dc.languageen
dc.publisherPergamon-elsevier Science Ltd
dc.publisherOxford
dc.publisherInglaterra
dc.relationApplied Mathematics Letters
dc.relationAppl. Math. Lett.
dc.rightsfechado
dc.rightshttp://www.elsevier.com/about/open-access/open-access-policies/article-posting-policy
dc.sourceWeb of Science
dc.subjectalgorithms
dc.subjectstatistical analysis
dc.subjectcomputational complexity
dc.subjectevolutionary computing
dc.titleA mixed evolutionary-statistical analysis of an algorithm's complexity
dc.typeArtículos de revistas


Este ítem pertenece a la siguiente institución