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Call for Participation (apologies for multiple copies) ## please forward it to those who may be interested ## -- TWO DAYS TO THE APPLICATION DEADLINE -- ----------------------------------------------------------------------- MESS 2018 - Metaheuristics Summer School - from Design to Implementation - 21-25 July 2018, Taormina, Italy https://www.ANTs-lab.it/mess2018/ [log in to unmask] ----------------------------------------------------------------------- ** APPLICATION DEADLINE: 15th April 2018 ** MESS 2018 is aimed at qualified and strongly motivated MSc and PhD students; post-docs; young researchers, and both academic and industrial professionals to provide an overview on the several metaheuristics techniques, and an in-depth analysis of the state-of-the-art. As first edition, MESS 2018 wants to analyze all metaheuristics from its designing to its implementation. In particular, in MESS 2018 will be analyzed modern heuristic methods for search and optimization problems, as well as the classical exact optimization methods, seen also in the metaheuristics context. All participants will have plenty of opportunities for debate and work with leaders in the field, benefiting from direct interaction and discussions in a stimulating environment. They will also have the possibility to present their recently results and/or their working in progress through oral or poster presentations, and interact with their scientific peers, in a friendly and constructive environment. ** Confirmed Speakers + Christian Blum, IIIA-CSIC, Barcelona, Spain - Lecture 1: Construct, Merge, Solve & Adapt: A Recent Hybrid Metaheuristic - Lecture 2: Swarm Intelligence in Combinatorial Optimization + Juergen Branke, Warwick Business School, UK - Lecture 1: Simulation-based optimisation - Lecture 2: Multi-objective optimisation + Salvatore Greco, University of Catania, Italy & University of Portsmouth, UK - Lecture 1: Basic Concepts of Multiple Criteria Decision Aiding/Making - Lecture 2: Using Preference Elicitation in Evolutionary Multiobjective Optimization + Yaochu Jin, University of Surrey, UK - Lecture 1: Data-driven surrogate-assisted evolutionary multi-objective optimization - Lecture 2: Multi-objective machine learning + Gunther Raidl, Technische Universitat Wien, Austria - Lecture 1: Pushing the Limits with Hybrid Metaheuristics - Lecture 2: Large Neighborhood Search Techniques + Helena Ramalhinho Lourenco, Universitat Pompeu Fabra, Spain - Lecture 1: Extensions of Iterated Local Search - Lecture 2: Issues and examples of applying metaheuristics in business + Mauricio Resende, AMAZON, USA - Lecture 1: Biased Random-Key Genetic Algorithms ? part I - Lecture 2: Biased Random-Key Genetic Algorithms ? part II + Celso Ribeiro, Universidade Federal Fluminense, Brazil - Lecture 1: Optimization by GRASP ? Part I - Lecture 2: Optimization by GRASP ? Part II + Andrea Schaerf, University of Udine, Italy - Lecture 1: Simulated Annealing and Friends: Introduction to Randomized Local Search Methods - Lecture 2: Simulated Annealing, we need to talk: Critical Analysis of Real-World Applications of Randomized Local Search Methods to Scheduling and Timetabling Problems + Fabio Schoen, Unviersity of Florence, Italy - Lecture 1: Large scale global optimization methods: basics - Lecture 2: Large scale global optimization methods: examples of applications + El-Ghazali Talbi, University of Lille 1, France - Lecture 1: TBA - Lecture 2: TBA + Daniele Vigo, University of Bologna, Italy - Lecture 1: Heuristic approaches for Vehicle Routing Problems - part I - Lecture 2: Heuristic approaches for Vehicle Routing Problems - part II ** Tutorial + Luca Di Gaspero, University of Udine, Italy Title: TBA + Paola Festa, University of Naples ?Federico II?, Italy Title: ?Successful applications of GRASP and its hybrids for hard combinatorial problems? ** Short Talk and Poster Presentation All participants may submit an abstract of their recent results, or works in progress, for presentation and having the opportunities for debate and interact with leaders in the field. Mini-Workshop Organizers and Scientific Committee will review the abstracts and will recommend for the format of the presentation (oral or poster). All abstracts will be published on the electronic hands-out book of the summer school. The Abstracts must be submitted by *April 15, 2018*. ** Sponsor & Scholarships: Thanks to the sponsorship by EU/ME METAHEURISTICS working group (https://www.euro-online.org/websites/eume/), some scholarships for the best participants will be available. More sponsors and scholarships will be announced soon!! ** Certificate: Participants will be delivered a certificate of attendance indicating the number of hours of lectures. The school will involve a total of 36-40 hours of lectures, which allows to all PhD, and master students to may get *8-ECTS* points. ** School Directors + Salvatore Greco, University of Catania, Italy + Panos Pardalos, University of Florida, USA + Mario Pavone, University of Catania, Italy + El-Ghazali Talbi, University of Lille 1, France + Daniele Vigo, University of Bologna, Italy ** Oral & Poster Presentation Organizers + Luca Di Gaspero, Unviersity of Udine, Italy + Paola Festa, University of Naples "Federico II", Italy ** Metaheuristics Competition Chair + Raffaele Cerulli, University of Salerno, Italy ** More Information: https://www.ANTs-lab.it/mess2018/ -- [log in to unmask] Facebook Group: https://www.facebook.com/groups/MetaheuristicsSchool/ Twitter: https://twitter.com/MESS_school -- Dr. Mario Pavone (PhD) Assistant Professor Department of Mathematics and Computer Science University of Catania V.le A. Doria 6 - 95125 Catania, Italy tel: 0039 095 7383034 fax: 0039 095 330094 Email: [log in to unmask] http://www.dmi.unict.it/mpavone/ =========================================================== MESS 2018 - Metaheuristics Summer School 21-25 July 2018, Taormina, Italy W: https://www.ANTs-lab.it/mess2018/ E: [log in to unmask] FB: https://www.facebook.com/groups/MetaheuristicsSchool/ Twitter: https://twitter.com/MESS_school ===========================================================