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Warning: those pages are outdated !

Since September 2001, when Marc Schoenauer moved to INRIA, this team has gradually vanished. But you might be interested by the activities of the TAO team, from both INRIA Futurs and LRI at Université de Paris-Sud Orsay where most people from the former EEAAX are now working. RIP.

The Artificial Evolution and Machine Learning Group (in french, EEAAX) is settled between two applied research labs, the Applied Maths Center (CMAP, URA CNRS 756) and the Solid Mechanics Lab (LMS, URA CNRS 317) at Ecole Polytechnique, one of the most famous french Engineering High Schools. Our team's originality comes from the coevolution of Artificial Intelligence techniques with the never ending sources of problems of Applied Maths and Structural Mechanics.


Research topics
  • Evolutionary Algorithms: Theory and Heuristics
    • Hitting times for simple EAs
    • Constraint handling
    • Grammar-based Genetic Programming
    • Multi-criteria optimisation
    • Iterated Genetic Algorithms.
    • Control of Genetic Operators by Inductive Learning.
    • Selection-Seduction Scheme.
  • Evolutionary Structural Analysis
    • Truss Structures Optimization.
    • Topological Shape Optimization.
    • Constitutive Equations Identification using Genetic Programming.
    • Identification of elastic inclusions
  • Identification
    • Neuro-Genetic Control by Multi-Layer Perceptrons.
    • Evolutionary System Identification by Recurrent Neural Nets.
    • Geophysical Profile Identification.
  • Optimisation
    • Direction Finders
    • Optical Multilayer Filters
    • Laser Lenses
    • Air Traffic Control problems (conflict resolution, space partitioning, global schedule allocation, ...).
  • Machine Learning
    • Polynomial Learning in Version Space.
    • Constraint-based Learning in FOL.
    • Learning Fuzzy rules.
    • Tradeoff Learning - Intelligibility.
    • Induction of a Similarity Function for CBR.
    • Application to GA-Control.
  • Evolutionary Software Engineering
    • The DREAM project (Distributed Ressource Evolutionary Algorithm Machine)
    • EvoLab and the EASEA language - EAsy Specification of Evolutionary Algorithms.
    • The EO (Evolving Objects) C++ Evolutionary Library.
 

Researchers

Software