The accurate prediction of time-changing covariances is an important problem in the modeling of multivariate financial data. However, some of the most popular models suffer from a) overfitting problems and multiple local optima, b) failure to capture shifts in market conditions and c) large computational costs. To address these problems we introduce a novel dynamic model for time-changing covar...

Diversity in evolutionary algorithms is a critical issue related to the performance obtained during the search process and strongly linked to convergence issues. The lack of the required diversity has been traditionally linked to problematic situations such as early stopping in the presence of local optima (usually faced when the number of individuals in the population is insufficient to deal w...

To solve problems with Local Search procedures, neighborhoods have to be defined. During the resolution, a solution is typically replaced by the best solution found in its neighborhood. A question concerns the size of the neighborhood. If a small neighborhood can be explored in polynomial time, a large neighborhood search may bring a faster convergence to a local optimum of good quality (see [1...

Fitness functions based on the Ising model are suited excellently for studying the adaption capabilities of randomised search heuristics. The one-dimensional Ising model was considered a hard problem for mutation-based algorithms, and the two-dimensional Ising model was even thought to amplify the difficulties. While in one dimension the Ising model does not have any local optima, in two dimens...