Call for Participation<\/p>\n
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\n– MESS 2020+1 – Metaheuristics Summer School
\n– Learning & Optimization from Big Data –
\n15-18 June 2021, Catania, Italy
\n(virtual and onsite mode)<\/p>\n
https:\/\/www.ANTs-lab.it\/mess2020\/<\/a> ** APPLICATION DEADLINE: 5th March 2021 **<\/p>\n https:\/\/www.ants-lab.it\/mess2020\/application\/<\/a><\/p>\n MESS 2020+1 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. The main theme of this edition is \u201cLearning and Optimization from Big Data\u201d, therefore MESS 2020+1 wants to focus on (i) Learning for Metaheuristics; (ii) Optimization in Machine Learning; and (iii) how Optimization and Learning affect the Metaheuristics making them relevant in handling Big Data.<\/p>\n Participants will be delivered a certificate of attendance indicating the number of hours of lectures (36-40 hours of lectures). In according to the academic system all PhD and master students attending to the summer school will may get 8 ECTS points.<\/p>\n ** LIST OF LECTURERS & LECTURES TITLES<\/p>\n + Paolo Arena, Unviersity of Catania, Italy + Angelo Cangelosi, University of Manchester & Alan Turing Institute, UK + Swagatam Das, Indian Statistical Institute, Kolkata + Luca Maria Gambardella, IDSIA Istituto Dalle Molle for Artificial Intelligence, Switzerland + Salvatore Greco, University of Catania, Italy & University of\u00a0Portsmouth, UK + Giuseppe F. Italiano, Luiss University, Italy + Andrea Lodi, Polytechnique Montr\u00e9al, Canada + Rafael Mart\u00ec, University of Valencia, Spain + Gabriela Ochoa, University of Stirling, UK + Mauricio Resende, AMAZON, USA + El-Ghazali Talbi, University of Lille 1, France + Daniele Vigo, University of Bologna, Italy ** METAHEURISTICS COMPETITION<\/p>\n All participants to the school will be involved in the \u201cMetaheuristics Competition\u201d, where each of them will must develop a metaheuristic solution on the given problem. The top three of the competition ranking will receive the MESS 2020+1 prize. Students whose algorithm will rank in the top ten of the competition ranking will be invited to submit a manuscript of their work to be published in the special volume MESS 2020+1 of the AIRO Springer Series.<\/p>\n ** SHORT TALK & POSTER PRESENTATION<\/p>\n 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.<\/p>\n The Abstracts must be submitted by *March 5, 2020*.<\/p>\n *See Previous Edition – MESS 2018*<\/p>\n https:\/\/www.ants-lab.it\/mess2018\/<\/a><\/p>\n ** MORE INFORMATION:<\/p>\n https:\/\/www.ANTs-lab.it\/mess2020\/<\/a>\u00a0\u00a0 —\u00a0\u00a0 mess.school@ANTs-lab.it<\/a><\/p>\n Facebook Group: https:\/\/www.facebook.com\/groups\/MetaheuristicsSchool\/<\/a><\/p>\n
\nmess.school@ANTs-lab.it<\/a>
\nhttps:\/\/www.facebook.com\/groups\/MetaheuristicsSchool\/<\/a>
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\nLecture#1: TBA
\nLecture#2: TBA<\/p>\n
\nLecture#1: Cognitive and Developmental Robotics, part 1
\nLecture#2: Cognitive and Developmental Robotics, part 2<\/p>\n
\nLecture#1: Deep Generative Adversarial Networks and Their Application to Class-imbalanced Learning
\nLecture#2: Non-convex Constrained Optimization – Some Advanced Approaches<\/p>\n
\nLecture#1: TBA
\nLecture#2:\u00a0 TBA<\/p>\n
\nLecture#1: Preference Learning in Multicriteria Decision Support
\nLecture#2: Evolutionary Multiobjective Optimization Guided by Preference Learning<\/p>\n
\nLecture#1: TBA
\nLecture#2: TBA<\/p>\n
\nLecture#1: TBA
\nLecture#2: TBA<\/p>\n
\nLecture#1: Optimization in Graph Drawing
\nLecture#2: Models and Heuristics in Discrete Diversity Maximization<\/p>\n
\nLecture#1: Fitness Landscape Analysis
\nLecture#2: Complex Networks in Search and Optimisation<\/p>\n
\nLecture#1: GRASP with Path-Relinking for Real-World Optimization Problems
\nLecture#2: Biased Random-Key Genetic Algorithms with Applications<\/p>\n
\nLecture#1: Machine learning for metaheuristics
\nLecture#2: Automated design of deep neural networks<\/p>\n
\nLecture#1: Fast and scalable heuristics for vehicle routing problems
\nLecture#2: Integrating machine learning into vehicle routing heuristics<\/p>\n