One of the goals of machine learning and data mining is to extract optimal hypotheses from (massive amounts of) data. What "optimal" means varies with the problem. The goal might be to induce useful knowledge, allowing new cases to be classified with optimal confidence (predictive data mining), or to synthetise the data into a set of understandable statements (descriptive data mining).

On the other hand, Evolutionary Computation and stochastic optimization are adapted to ill-posed optimization problems, such as involved in machine learning, data mining, identification, optimal policies, and inverse problems. However, optimization algorithms must adapt themselves to the search landscape; in other words, they need learning
capabilities.

The Learning and Evolution Group was born in 2003 from the convergence of the above research fields, merging the Inference and Learning Group from LRI-CNRS, and researchers from Evolutionary Computation from INRIA. It benefits from the expertise gathered in the previous Evolution and Learning Group in Ecole Polytechnique (1986-2002), applying Artificial Intelligence techniques to ill-posed problems from Numerical Engineering.