Mateda-2.0: Estimation of Distribution Algorithms in MATLAB

This paper describes Mateda-2.0, a MATLAB package for estimation of distribution algorithms (EDAs). This package can be used to solve single and multi-objective discrete and continuous optimization problems using EDAs based on undirected and directed probabilistic graphical models. The implementation contains several methods commonly employed by EDAs. It is also conceived as an open package to allow users to incorporate different combinations of selection, learning, sampling, and local search procedures. Additionally, it includes methods to extract, process and visualize the structures learned by the probabilistic models. This way, it can unveil previously unknown information about the optimization problem domain. Mateda-2.0 also incorporates a module for creating and validating function models based on the probabilistic models learned by EDAs.

The effects of disorder in dimerized quantum magnets in mean field approximations

15 p.

On the Benefits of Including Age-structure in Harvest Control Rules

This paper explores the benefits of including age-structure in the control rule (HCR) when decision makers regard their (age-structured) models as approximations. We find that introducing age structure into the HCR reduces both the volatility of the spawning biomass and the yield. Although at a fairly imprecise level the benefits are lower, there are still major advantages for actual assessment precision of the case study. Moreover, we find that when age-structure is included in the HCR the relative ranking of different policies in terms of variance in biomass and yield does not differ. These results are shown both theoretically and numerically by applying the model to the Southern Hake fishery.