Search for contacts, projects,
courses and publications

Artificial Intelligence (MSc)


Gambardella L. M.

Course director

Bedi N. S.


Encz K. I.



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

Teaching mode

In presence

Learning methods

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

Examination information

Written test and code evaluation