ALEA Research team

Advanced Learning Evolutionary Algorithms

Team presentation

The recent technological advances together with the fast development of probability theory have lead to new generations of sophisticated evolutionary type and interacting stochastic processes for analyzing more and more realistic models arising in engineering and computer sciences. To name a few, bootstrap filters, genetic and tabu searches, quantum Monte Carlo walkers, ant colonies and particle swarm intelligence, and many others. These biology-inspired algorithms are often presented as natural heuristic simulation schemes without any mathematical foundations, nor a single performance analysis ensuring their convergence, nor even a single theoretical or physical discussion that clarifies the applicability of these models. Our project consists in studying their mathematical foundations, their different application areas, the design of new methodologies, as well as their computer implementation. Our project is not a single application-driven project, but it is oriented towards concrete applications with important potential industrial transfers on two central problems in advanced stochastic engineering ; namely, Bayesian inference and rare event simulation, and more particularly unsupervised learning, multi-target tracking, data assimilation, epidemic and micro-biology predictions.

Research themes

Our research project is centered on three central problems in advanced stochastic engineering
  • Bayesian inference
  • rare event simulation
  • Global optimization
and more particularly
  • Unsupervised learning
  • Nonlinear filtering and multi-target tracking
  • Data assimilation and forecasting
  • Epidemiology and infection spreads inference
These important and natural research directions have emerged as logical parts of the team project combined with interdisciplinary approaches well-represented at Bordeaux university campus. The fundamental and the theoretical aspects of our research project are essentially concerned with the stochastic analysis of the following three classes of biology inspired stochastic algorithms :
  • Branching and interacting particle systems
  • Reinforced random walks and selfinteracting processes
  • Random tree based search models
  • l'analyse de risques et prédiction d'épidémies

International and industrial relations

International collaborations:
  • Dan Crisan and Ajay Jasra (Imperial Collage of London)
  • Bruno Rémillard (HEC Montreal)
  • Arnaud Doucet (Institute of Mathematical statistics, Tokyo)
  • Andreas Greven (Erlangen Univ.)
  • Li-Ming Wu (Clermont Ferrand Univ. and Wuhan Univ.)
  • Pierre Tarres and Chris Holmes (Oxford University)
  • Persi Diaconis and Susan Holmes (University of Stanford)
Industrial collaborations:
  • DCNS-SIS, on multi-target tracking
  • CEA CESTA, on the statistical modeling of electromagnetic fields and stochastic optimization techniques
  • EDF, on recursive prediction algorithms and Monte Carlo methods in financial mathematics

Keywords: Monte Carlo algorithms Interacting stochastic processes Particle filters Rare event simulation Bayesian inference Unsupervised learning

Research teams of the same theme :

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