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.
- Responsable de cours: Benoit, Alexandre
- Responsable de cours: BERSANI, CHIARA
- Responsable de cours: Galichet, Sylvie
- Responsable de cours: Monnet, Sebastien
- Responsable de cours: ZERO, ENRICO