Artificial Intelligence (MSc)
The aim of the course is to present the most modern techniques for solving complex problems. We focus on state of the art meta-heuristic for continuous function and combinatorial optimization: among the methods we deepen simulated annealing, genetic algorithms, variable neighborhood search and ant colony optimization. Gaming with two players will be also discussed as the integration between meta-heuristics and machine learning. Students are asked to implement and test some of these techniques.
Meta-Heuristics algorithms learning and testing
Front lecture, text reading, code development For each topic you receive a paper to read and to support to your studies Simulated Annealing SA extension ILS: iterated local search Genetic Algorithms GA variant PBIL population-based incremental learning Ant Colony Optimization ACS for vehicle routing problems Tabu search
Written test and code evaluation