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
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
- Enseignant (auteur): Monnet Sébastien
Plateformes collaboratives (ISOC 631) Economie et gouvernance de la donnée (ISOC 731 Sécurité et cryptographie (INFO 731)
For a long time, data in companies were only reused to find information about a customer, a product, but now, companies understand these data are valuable. Two options seem possible for companies: (1) refine this data deluge to understand better their customers or (2) offer these data to external companies for marketing campaigns for example.
It is then crucial to manage how data are acquired (with prior customer consent), curated and analyzed. The objective of this project around social network is to define security policies and data privacy for these data.