The ORKAD team aims to exploit simultaneously expertise in combinatorial optimization and knowledge extraction to address upcoming optimization problems. While the two scientific areas are developed more or less independently, the synergy between combinatorial optimization and knowledge extraction offers an opportunity first, to improve the performance and autonomy of optimization methods thanks to Knowledge and secondly to solve efficiently Knowledge extraction problems thanks to operational research methods. Our approaches are mainly based on mono and multi-objective combinatorial optimization and led to the development of open source software.
Laetitia Jourdan
Nouveaux algorithmes de prédiction et de planification pour le digital learning basés sur des méthodes d'optimisation
Optimization for automatic design and cofiguration of datamining algorithms : Application to medical data
MO-AutoML : un framework multiobjectif pour la configuration automatique d'algorithmes de machine learning
Automatic Design of Dynamic Local Search Algorithms
Méthodes d'optimisation adaptatives pour l'optilisation de la chaîne d'approvisionnement
Appariement de descripteurs évoluant dans le temps. Application à la comparaison d'assurance 03/04/2019
Optimizing competitive economic decisions in a business game 13/10/2017
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