{"id":1395,"date":"2017-03-09T12:09:33","date_gmt":"2017-03-09T11:09:33","guid":{"rendered":"http:\/\/cio.umh.es\/?p=1395"},"modified":"2017-03-09T12:09:33","modified_gmt":"2017-03-09T11:09:33","slug":"conference-prof-talbi","status":"publish","type":"post","link":"https:\/\/cio.umh.es\/en\/2017\/03\/09\/conference-prof-talbi\/","title":{"rendered":"Conference Prof. Talbi"},"content":{"rendered":"<p>[:es]<span style=\"color: #000000\"><strong>Tittle:<\/strong><span class=\"Apple-converted-space\">\u00a0Combining metaheuristics with mathematical programming and machine learning<\/span><\/span><br \/>\n<span style=\"color: #000000\"><strong>Speaker:\u00a0<span lang=\"EN-US\">\u00a0<\/span><\/strong><span lang=\"EN-US\">El-Ghazali Talbi \u00a0(University of Lille 1)<\/span><\/span><br \/>\n<span style=\"color: #000000\"><strong>Date:<\/strong>\u00a011\/04\/2017 12:30h<\/span><br \/>\n<span style=\"color: #000000\"><strong>Location:<\/strong> Sala de Seminarios, Edificio Torretamarit<\/span><\/p>\n<p style=\"text-align: justify\"><span style=\"color: #000000\"><strong>Abstract:<\/strong><\/span><br \/>\n<span style=\"color: #000000\"> During the last years, interest on hybrid metaheuristics has risen considerably in the field of optimization and machine learning. The best results found for many optimization problems in science and industry are obtained by hybrid optimization algorithms. Combinations of optimization tools such as metaheuristics, mathematical programming, constraint programming and machine learning, have provided very efficient optimization algorithms. Four different types of combinations are considered in this talk: (i) Combining metaheuristics with complementary metaheuristics. (ii) Combining metaheuristics with exact methods from mathematical programming approaches which are mostly used in the operations research community. (iii) Combining metaheuristics with machine learning and data mining techniques.<\/span><\/p>\n<p>[:en]<strong>Tittle:<\/strong><span class=\"Apple-converted-space\">\u00a0Combining metaheuristics with mathematical programming and machine learning<\/span><br \/>\n<strong>Speaker:\u00a0<span lang=\"EN-US\">\u00a0<\/span><\/strong><span lang=\"EN-US\">El-Ghazali Talbi \u00a0(University of Lille 1)<\/span><br \/>\n<strong>Date:<\/strong>\u00a011\/04\/2017 12:30h<br \/>\n<strong>Location:<\/strong> Sala de Seminarios, Edificio Torretamarit<br \/>\n<strong>Abastract:<\/strong><br \/>\nDuring the last years, interest on hybrid metaheuristics has risen considerably in the field of optimization and machine learning. The best results found for many optimization problems in science and industry are obtained by hybrid optimization algorithms. Combinations of optimization tools such as metaheuristics, mathematical programming, constraint programming and machine learning, have provided very efficient optimization algorithms. Four different types of combinations are considered in this talk: (i) Combining metaheuristics with complementary metaheuristics. (ii) Combining metaheuristics with exact methods from mathematical programming approaches which are mostly used in the operations research community. (iii) Combining metaheuristics with machine learning and data mining techniques.[:]<\/p>","protected":false},"excerpt":{"rendered":"<p>[:es]Tittle:\u00a0Combining metaheuristics with mathematical programming and machine learning<br \/>\nSpeaker:\u00a0\u00a0El-Ghazali Talbi \u00a0(University of Lille 1)<br \/>\nDate:\u00a011\/04\/2017 12:30h<br \/>\nLocation: Sala de Seminarios, Edificio Torretamarit<br \/>\nAbstract:<br \/>\n During the last years, interest on hybrid metaheuristics has risen considerably in the field of optimization and machine learning. The best results found for many optimization problems in science and industry are obtained by hybrid optimization [&#8230;]<\/p>","protected":false},"author":3477,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_links_to":"","_links_to_target":""},"categories":[4,873],"tags":[],"_links":{"self":[{"href":"https:\/\/cio.umh.es\/en\/wp-json\/wp\/v2\/posts\/1395"}],"collection":[{"href":"https:\/\/cio.umh.es\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/cio.umh.es\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/cio.umh.es\/en\/wp-json\/wp\/v2\/users\/3477"}],"replies":[{"embeddable":true,"href":"https:\/\/cio.umh.es\/en\/wp-json\/wp\/v2\/comments?post=1395"}],"version-history":[{"count":0,"href":"https:\/\/cio.umh.es\/en\/wp-json\/wp\/v2\/posts\/1395\/revisions"}],"wp:attachment":[{"href":"https:\/\/cio.umh.es\/en\/wp-json\/wp\/v2\/media?parent=1395"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/cio.umh.es\/en\/wp-json\/wp\/v2\/categories?post=1395"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/cio.umh.es\/en\/wp-json\/wp\/v2\/tags?post=1395"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}