Course information

 This course aims to address multi-criteria problems from two different points of view: optimization and decision aiding. In both cases, the general problem is presented before detailing the different approaches.Multi-criteria optimization is approached by evolutionary algorithms (genetic algorithms, genetic programming). The different components of artificial evolution are presented before discussing multi-criteria optimization using dominance-based approaches and presenting the Non-dominated Sorting Genetic Algorithm (NSGA) algorithm. Multi-criteria decision aiding is widely used in decision problems to find the "best possible" alternative solution, making the process more explicit, rational and efficient. The decision maker is helped by automatic tools to construct one or more preference models. Addressed problems and modeling approaches lead to various methods and tools presented.