Using landscape topology to compare continuous metaheuristics: a framework and case study on EDAs and ridge structure

In this paper we extend a previously proposed randomized landscape generator in combination with a comparative experimental methodology to study the behavior of continuous metaheuristic optimization algorithms. In particular, we generate twodimensional landscapes with parameterized, linear ridge structure, and perform pairwise comparisons of algorithms to gain insight into what kind of problems are easy and difficult for one algorithm instance relative to another.We apply thismethodology to investigate the specific issue of explicit dependency modeling in simple continuous estimation of distribution algorithms. Experimental results reveal specific examples of landscapes (with certain identifiable features) where dependency modeling is useful, harmful, or has little impact on mean algorithm performance. Heat maps are used to compare algorithm performance over a large number of landscape instances and algorithm trials. Finally, we perform ameta-search in the landscape parameter space to find landscapes which maximize the performance between algorithms. The results are related to some previous intuition about the behavior of these algorithms, but at the same time lead to new insights into the relationship between dependency modeling in EDAs and the structure of the problem landscape. The landscape generator and overall methodology are quite general and extendable and can be used to examine specific features of other algorithms

White matter microstructural changes in short-term learning of a continuous visuomotor sequence

Efficient neural transmission is crucial for optimal brain function, yet the plastic potential of white matter (WM) has long been overlooked. Growing evidence now shows that modifications to axons and myelin occur not only as a result of long-term learning, but also after short training periods. Motor sequence learning (MSL), a common paradigm used to study neuroplasticity, occurs in overlapping learning stages and different neural circuits are involved in each stage. However, most studies investigating short-term WM plasticity have used a pre-post design, in which the temporal dynamics of changes across learning stages cannot be assessed. In this study, we used multiple magnetic resonance imaging (MRI) scans at 7 T to investigate changes in WM in a group learning a complex visuomotor sequence (LRN) and in a control group (SMP) performing a simple sequence, for five consecutive days. Consistent with behavioral results, where most improvements occurred between the two first days, structural changes in WM were observed only in the early phase of learning (d1-d2), and in overall learning (d1-d5). In LRNs, WM microstructure was altered in the tracts underlying the primary motor and sensorimotor cortices. Moreover, our structural findings in WM were related to changes in functional connectivity, assessed with resting-state functional MRI data in the same cohort, through analyses in regions of interest (ROIs). Significant changes in WM microstructure were found in a ROI underlying the right supplementary motor area. Together, our findings provide evidence for highly dynamic WM plasticity in the sensorimotor network during short-term MSL