Estimation and modelling problems as they arise in many fields often turn out to be intractable by standard numerical methods. One way to deal with such a situation consists in simplifying models and procedures. However, the solutions to these simplified problems might not be satisfying. A different approach consists in applying optimization heuristics such as trajectory methods (e.g., simulated annealing, threshold accepting, tabu search), population based methods (e.g., genetic algorithms, differential evolution, particle swarm) or hybrid methods (e.g., memetic algorithms), which have been developed over the last two decades. Although the use of these methods increases in estimation and modelling, there is scope for further applications and a more detailed analysis of the statistical properties of these algorithms.
This track concerns on one hand the computational aspects of estimation and modelling problems, in particular for highly complex cases, when standard algorithms fail. It will also cover real applications in cross-disciplinary fields. On the other hand, the track contributes to the development and evaluation of optimization heuristics in modelling and estimation applications. In particular, it will strive for establishing standards for the tuning of algorithms and for reporting solutions including all necessary information on algorithmic implementation. Furthermore, the statistics of the results obtained by means of optimization algorithms will be analyzed.