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We investigate the magnetism of the Co$^{II}_4$(OH)$_2$(C$_1$$_0$H$_1$$_6$O$_4$)$_3$ metal-organic framework which displays complex inorganic chains separated from each other by distances of 1 to 2 nm, and which orders at ~5.4 K. The zero-field magnetic structure is determined using neutron powder diffraction: it is mainly antiferromagnetic but posseses a ferromagnetic component along the $\textbf{c}$-axis. This magnetic structure persists in presence of a magnetic field. Ac susceptibility measurements confirm the existence of a single thermally activated regime over 7 decades in frequency ($E/k_B\approx64 K$) whereas time-dependent relaxation of the magnetization after saturation in an external field leads to a two times smaller energy barrier. These experiments probe the slow dynamics of domain walls within the chains: we propose that the ac measurements are sensitive to the motion of existing domain walls within the chains, while the magnetization measurements are governed by the creation of domain walls., Comment: 12 pages, 14 figures

This paper proposes a self-adaptation mechanism to manage the resources allocated to the different species comprising a cooperative coevolutionary algorithm. The proposed approach relies on a dynamic extension to the well-known multi-armed bandit framework. At each iteration, the dynamic multi-armed bandit makes a decision on which species to evolve for a generation, using the history of progress made by the different species to guide the decisions. We show experimentally, on a benchmark and a real-world problem, that evolving the different populations at different paces allows not only to identify solutions more rapidly, but also improves the capacity of cooperative coevolution to solve more complex problems., Comment: Accepted at GECCO 2013

Geometric discrepancies are standard measures to quantify the irregularity of distributions. They are an important notion in numerical integration. One of the most important discrepancy notions is the so-called \emph{star discrepancy}. Roughly speaking, a point set of low star discrepancy value allows for a small approximation error in quasi-Monte Carlo integration. It is thus the most studied discrepancy notion. In this work we present a new algorithm to compute point sets of low star discrepancy. The two components of the algorithm (for the optimization and the evaluation, respectively) are based on evolutionary principles. Our algorithm clearly outperforms existing approaches. To the best of our knowledge, it is also the first algorithm which can be adapted easily to optimize inverse star discrepancies., Comment: Extended abstract appeared at GECCO 2013. v2: corrected 3 numbers in table 4