{"id":116828,"date":"2014-12-12T00:00:00","date_gmt":"2014-12-12T00:00:00","guid":{"rendered":"https:\/\/www.deberes.net\/tesis\/sin-categoria\/solving-permutation-problems-with-estimation-of-distribution-algorithms-and-extensions-thereof\/"},"modified":"2014-12-12T00:00:00","modified_gmt":"2014-12-12T00:00:00","slug":"solving-permutation-problems-with-estimation-of-distribution-algorithms-and-extensions-thereof","status":"publish","type":"post","link":"https:\/\/www.deberes.net\/tesis\/inteligencia-artificial\/solving-permutation-problems-with-estimation-of-distribution-algorithms-and-extensions-thereof\/","title":{"rendered":"Solving permutation problems with estimation of distribution algorithms and extensions thereof"},"content":{"rendered":"

Tesis doctoral de Josu Ceberio Uribe <\/strong><\/h2>\n

La tesis est\u00e1 dedicada al estudio y resoluci\u00f3n de problemas de optimizaci\u00f3ncombinatoria basadas en permutaciones. Dichos problemas se caracterizan por el grannumero de posibles soluciones que pueden tomar: n!. Como consecuencia, la mayor\u00edade los metodos de optimizaci\u00f3n exactos no son efectivos a la hora de resolverproblemas de este tipo. En este sentido, la comunidad cient\u00edficia ha propuesto un grann\u00famero de m\u00e9todos heur\u00edsticos y metaheur\u00edsticos para resolverlos.A lo largo del documento se desarrollan tres lineas de investigaci\u00f3n diferenciadas quetienen como eje fundamental proponer avances en la resoluci\u00f3n de los problemas depermutaciones. Una primera linea se encuadra en desarrollar algoritmos de estimaci\u00f3nde distribuciones. La gran mayor\u00eda de este tipo de algoritmos no implementan modelosprobabil\u00edsticos eficaces para modelar permutaciones. En este sentido, se han estudiadolos modelos probabil\u00edsticos de mallows, generalized mallows y plackett-luce, que, ala vista de los experimentos, suponen un paso adelante respecto a los algoritmosexistentes. La segunda linea de investigaci\u00f3n, aborda los problemas de permutacionesdesde el \u00e1mbito de la b\u00fasqueda local y an\u00e1lisis de fitness landscapes. En particular, setoma el problema de ordenaci\u00f3n lineal como caso de estudio, y extraemoscaracter\u00edsticas del problema, que permiten hacer una b\u00fasqueda m\u00e1s eficiente. Por\u00faltimo, la tercera linea de investigaci\u00f3n profundiza en la descomposici\u00f3n de la funci\u00f3nasociada al problema en subfunciones elementales. Dicha descomposici\u00f3n permiteconocer mejor la estructura del problema, y en nuestro caso, multiobjectivizar elproblema reduciendo as\u00ed su complejidad y facilitando su optimizaci\u00f3n.(No rebasar en extensi\u00f3n el presente recuadro)<\/p>\n

 <\/p>\n

Datos acad\u00e9micos de la tesis doctoral \u00abSolving permutation problems with estimation of distribution algorithms and extensions thereof<\/strong>\u00ab<\/h3>\n
    \n
  • T\u00edtulo de la tesis:<\/strong>\u00a0 Solving permutation problems with estimation of distribution algorithms and extensions thereof <\/li>\n
  • Autor:<\/strong>\u00a0 Josu Ceberio Uribe <\/li>\n
  • Universidad:<\/strong>\u00a0 Pa\u00eds vasco\/euskal herriko unibertsitatea<\/li>\n
  • Fecha de lectura de la tesis:<\/strong>\u00a0 12\/12\/2014<\/li>\n<\/ul>\n

     <\/p>\n

    Direcci\u00f3n y tribunal<\/h3>\n
      \n
    • Director de la tesis<\/strong>\n
        \n
      • Alexander Mendiburu Alberro<\/li>\n<\/ul>\n<\/li>\n
      • Tribunal<\/strong>\n
          \n
        • Presidente del tribunal: Jos\u00e9 Antonio Gamez martin <\/li>\n
        • christian Blum (vocal)<\/li>\n
        • john Mccall — (vocal)<\/li>\n
        • Mar\u00eda Jos\u00e9 Del Jes\u00fas d\u00edaz (vocal)<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n

           <\/p>\n","protected":false},"excerpt":{"rendered":"

          Tesis doctoral de Josu Ceberio Uribe La tesis est\u00e1 dedicada al estudio y resoluci\u00f3n de problemas de optimizaci\u00f3ncombinatoria basadas en […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""}},"footnotes":""},"categories":[37303,2528,12909],"tags":[118477,212473,230486,40393,230485,49576],"class_list":["post-116828","post","type-post","status-publish","format-standard","hentry","category-heuristica","category-inteligencia-artificial","category-pais-vasco-euskal-herriko-unibertsitatea","tag-alexander-mendiburu-alberro","tag-christian-blum","tag-john-mccall","tag-jose-antonio-gamez-martin","tag-josu-ceberio-uribe","tag-maria-jose-del-jesus-diaz"],"_links":{"self":[{"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/posts\/116828","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/comments?post=116828"}],"version-history":[{"count":0,"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/posts\/116828\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/media?parent=116828"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/categories?post=116828"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/tags?post=116828"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}