A bridge between Probability, Set Oriented Numerics and Evolutionary Computation


Conference organized by


University of Luxembourg

INRIA Bordeaux Sud-Ouest


August 7-9 (Tuesday-Thursday), 2012
Mexico City, México


The massive use and large applicability spectrum of evolutionary algorithms for real-life applications determined the need of establishing solid theoretical grounds. Only to offer a few examples, one may consider mathematical objects th at are sometimes difficult and/or costly to calculate. At the same time, acknowledged new results show that evolutionary computation can provide in some cases good and fast estimators of these quantities. Similarly, the handling of large quantities of data may require the use of distributed environments where the probability of failure and the stability of the algorithms may need to be addressed. What is more, common practice confirms in many cases that theory based results have the advantage of ensuring performance guarantee factors for evolutionary algorithms in areas as diverse as optimization, bio-informatics or robotics. 

The aim of the EVOLVE is to build a bridge between probability, statistics, set oriented numerics and evolutionary computing, as to identify new common and challenging research aspects. The conference is also intended to foster a growing interest for robust and efficient methods with a sound theoretical background. EVOLVE is intended to unify theory-inspired methods and cutting-edge techniques ensuring performance guarantee factors. By gathering researchers with different backgrounds, ranging from computer science to mathematics, statistics and physics, to name just a few, a unified view and vocabulary can emerge where the theoretical advancements may echo in different domains.

Summarizing, the EVOLVE focuses on challenging aspects arising at the passage from theory to new paradigms and aims to provide a unified view while raising questions related to reliability, performance guarantees and modeling.




  • theoretical foundations of evolutionary type algorithms
  • models in biology: branching processes, dynamic population models.
  • stochastic algorithms: simulated annealing, particle models, and Markov chain Monte Carlo methods, adaptive Monte Carlo, quantum Monte Carlo methods, MCMC methods, particle methods
  • risk analysis: rare events simulation, sensitivity measures
  • applications: bayesian statistics, large networks analysis, models in fluid mechanics, financial mathematics, molecular chemistry, bacteriology, epidemiology
  • set oriented numerics
  • stochastic optimization
  • landscape analysis
  • self-tuning and self-adaptive techniques
  • single-, multi- and many-objective optimization
  • large scale, highly multi-modal or high-dimensional problems
  • bio-inspired models: evolutionary programming, swarm intelligence