{"id":1219,"date":"2023-03-07T17:51:17","date_gmt":"2023-03-07T16:51:17","guid":{"rendered":"https:\/\/www.uni.lu\/snt-fr\/events\/phd-defence-learning-optimisation-algorithms-over-graphs\/"},"modified":"2023-03-07T17:51:17","modified_gmt":"2023-03-07T16:51:17","slug":"phd-defence-learning-optimisation-algorithms-over-graphs","status":"publish","type":"events","link":"https:\/\/www.uni.lu\/snt-fr\/events\/phd-defence-learning-optimisation-algorithms-over-graphs\/","title":{"rendered":"PhD Defence: Learning Optimisation Algorithms over Graphs"},"content":{"rendered":"<section class=\"wp-block-unilux-blocks-free-section section\"><div class=\"container xl:max-w-screen-xl\"><p>You are cordially invited to join the <strong>PhD defence of Gabriel Valentin DUFLO<\/strong> on <strong>Friday, 31 March at 10:00 on Belval campus, MSA, room 2.380.<\/strong><\/p><p><strong>Members of the defense committee:<\/strong><\/p><ul class=\"ulux-list\"><li class=\"ulux-list-item\">Prof. Dr Pascal BOUVRY, University of Luxembourg, Chairman<\/li><li class=\"ulux-list-item\">Prof. Dr El-Ghazali TALBI University of Lille &#8211; INRIA &#8211; CNRS\/France, Deputy Chairman<\/li><li class=\"ulux-list-item\">Dr Gr\u00e9goire DANOY, University of Luxembourg, Supervisor<\/li><li class=\"ulux-list-item\">Prof. Dr Ann NOW\u00c9, VRIJE UNIVERSITEIT BRUSSEL (VUB), Belgium, Member<\/li><li class=\"ulux-list-item\">Prof. Dr Roland BOUFFANAIS, University of Ottawa, Canada, Member<\/li><\/ul><p><strong>Abstract:<\/strong><\/p><p>The paradigm of learning to optimise relies on the following principle: instead of designing an algorithm to solve a problem, we design an algorithm which will automate the design of such a solver. Hyper-heuristics constitute the main learning-to-optimise techniques. They are however designed to tackle a specific problem. Due to this lack of generality, existing works fully redesign hyper-heuristics when tackling a new problem, despite the fact that they may share a similar structure. In this dissertation, we tackle this challenge by proposing a generic way for learning to optimise any problem. To this end, this thesis introduces three main contributions: (i) an analysis of the formal functioning of learning-to-optimise techniques; (ii) a model of generic hyper-heuristic, named Algorithm Learner for Graph Optimisation problems (ALGO), constituting the central point of this work; (iii) a real-world use case where we use our generic hyper-heuristic to automate the design of behaviours within a swarm of drones.<\/p><\/div><\/section>","protected":false},"excerpt":{"rendered":"<p>You are cordially invited to join the PhD defence of Gabriel Valentin DUFLO on Friday, 31 March at 10:00 on Belval campus, MSA, room 2.380.<\/p>\n","protected":false},"author":0,"featured_media":1220,"parent":0,"menu_order":0,"comment_status":"open","ping_status":"closed","template":"","format":"standard","meta":{"featured_image_focal_point":[],"show_featured_caption":false,"ulux_newsletter_groups":"","uluxPostTitle":"","uluxPrePostTitle":"","_trash_the_other_posts":false,"_price":"","_stock":"","_tribe_ticket_header":"","_tribe_default_ticket_provider":"","_tribe_ticket_capacity":"0","_ticket_start_date":"","_ticket_end_date":"","_tribe_ticket_show_description":"","_tribe_ticket_show_not_going":false,"_tribe_ticket_use_global_stock":"","_tribe_ticket_global_stock_level":"","_global_stock_mode":"","_global_stock_cap":"","_tribe_rsvp_for_event":"","_tribe_ticket_going_count":"","_tribe_ticket_not_going_count":"","_tribe_tickets_list":"[]","_tribe_ticket_has_attendee_info_fields":false,"event_start_date":"2023-03-31 10:00:00","event_end_date":"2023-03-31 12:00:00","event_speaker_name":"Gabriel Valentin DUFLO","event_speaker_link":"","event_is_online":false,"event_location":"Belval, MSA, room 2.380","event_street":"","event_location_link":"","event_zip_code":"","event_city":"","event_country":"LU"},"events-topic":[],"events-type":[],"organisation":[183],"authorship":[],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v22.3 (Yoast SEO v22.3) - 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