A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computing

EVOLVE 2014

The aim of the EVOLVE international conference is to build a bridge between probability, statistics, set oriented numerics and evolutionary computing, as to identify new common and challenging research aspects.

The event is also intended to foster a growing interest for robust and efficient new methods with a sound theoretical background and, last but not least, to unify theory-inspired methods and cutting-edge techniques that ensure performance guarantees. By gathering researchers with different backgrounds, e.g. ranging from computer science to mathematics, statistics and physics, to name just a few, a unified view and vocabulary can emerge where theoretical advancements may echo in different domains.

The wide use and large applicability spectrum of evolutionary algorithms in real-life applications also determined a need for establishing solid theoretical grounds. Only to offer one example, one may consider mathematical objects that are sometimes difficult and/or costly to calculate; acknowledged new results shown that evolutionary algorithms can, in some cases, act as good and fast estimators. 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. Summarizing, EVOLVE focuses on challenging aspects arising at the passage from theory to new paradigms and practice, thus aiming to provide a unified view while, at the same time, raising questions related to reliability, performance guarantees and modeling. 

Scope & Topics

Monte Carlo Methods, Mean Field Particle Models, Set-Oriented Numerics, and Evolutionary Algorithms arise in a variety of fields, and it is the aim of the conference to bring people from these areas together to discuss new results and trends and promote collaboration across these disciplines.

Among other aspects and while not restricted to the following, topics include:

Probability and Statistics 

Monte Carlo Methods, Markov Chain Monte Carlo Methods, Sequential Monte Carlo Methods, Mean Field Particle Models, Branching and Interacting Particle Processes, Nonlinear Markov Processes, Self Interacting Processes, Reinforcement Models, Game Theory, Stochastic Control Theory

Statistics and Machine Learning

Bayesian Inference, Hidden Markov Chain Models, Statistical Machine Learning Techniques. Calibration and Uncertainty Propagations in Numerical Codes, Nonlinear Filtering, Multiple Object Tracking Models.

Set Oriented Numerics and Natural Computing 

Set-Oriented Numerics, Multicriteria Optimization, Level Set Algorithms, Genetic Algorithms, Landscape Theory, Natural Computing, Evolutionary Algorithms, Robust Design, Computational Vision, Estimation of Distribution Algorithms