Alternative Restart Strategies for CMA-ES

This paper focuses on the restart strategy of CMA-ES on multi-modal functions. A first alternative strategy proceeds by decreasing the initial step-size of the mutation while doubling the population size at each restart. A second strategy adaptively allocates the computational budget among the restart settings in the BIPOP scheme. Both restart strategies are validated on the BBOB benchmark; their generality is also demonstrated on an independent real-world problem suite related to spacecraft trajectory optimization

Developing of the smart textile for energy expenditure monitoring

Peer ReviewedPostprint (published version

Exploring jet-launching conditions for SFXTs

In the magneto-centrifugal mechanism for jet formation, accreting neutron stars are assumed to produce relativistic jets only if their surface magnetic field is weak enough ($B \sim 10^8$ G). However, the most common manifestation of neutron stars are pulsars, whose magnetic field distribution peaks at $B \sim 10^{12}$ G. If the neutron star magnetic field has at least this strength at birth, it must decay considerably before jets can be launched in binary systems. We study the magnetic field evolution of a neutron star that accretes matter from the wind of a high-mass stellar companion so that we can constrain the accretion rate and the impurities in the crust, which are necessary conditions for jet formation. We solved the induction equation for the diffusion and convection of the neutron star magnetic field confined to the crust, assuming spherical accretion in a simpliflied one-dimensional treatment. We incorporated state-of-the-art microphysics, including consistent thermal evolution profiles, and assumed two different neutron star cooling scenarios based on the superfluidity conditions at the core. We find that in this scenario, magnetic field decay at long timescales is governed mainly by the accretion rate, while the impurity content and thermal evolution of the neutron star play a secondary role. For accretion rates $\dot{M}\gtrsim10^{-10}$ M$_{\odot}$ yr$^{-1}$, surface magnetic fields can decay up to four orders of magnitude in $\sim$10$^7$ yr, which is the timescale imposed by the evolution of the high-mass stellar companion in these systems. Based on these results, we discuss the possibility of transient jet-launching in strong wind-accreting high-mass binary systems like supergiant fast X-ray transients.Comment: 8 pages, 8 figures. Accepted for publication in A&

Enhancement of the critical temperature in iron-pnictide superconductors by finite size effects

Recent experiments have shown that, in agreement with previous theoretical predictions, superconductivity in metallic nanostructures can be enhanced with respect to the bulk limit. Motivated by these results we study finite size effects (FSE) in an iron-pnictide superconductor. For realistic values of the bulk critical temperature Tc ~ 20-50K, we find that, in the nanoscale region L ~ 10 nm, Tc(L) has a complicated oscillating pattern as a function of the system size L. A substantial enhancement of Tc with respect to the bulk limit is observed for different boundary conditions, geometries and two microscopic models of superconductivity. Thermal fluctuations, which break long range order, are still small in this region. Finally we show that the differential conductance, an experimental observable, is also very sensitive to FSE.Comment: 4 pages, 3 figure

CIXL2: A Crossover Operator for Evolutionary Algorithms Based on Population Features

In this paper we propose a crossover operator for evolutionary algorithms with real values that is based on the statistical theory of population distributions. The operator is based on the theoretical distribution of the values of the genes of the best individuals in the population. The proposed operator takes into account the localization and dispersion features of the best individuals of the population with the objective that these features would be inherited by the offspring. Our aim is the optimization of the balance between exploration and exploitation in the search process. In order to test the efficiency and robustness of this crossover, we have used a set of functions to be optimized with regard to different criteria, such as, multimodality, separability, regularity and epistasis. With this set of functions we can extract conclusions in function of the problem at hand. We analyze the results using ANOVA and multiple comparison statistical tests. As an example of how our crossover can be used to solve artificial intelligence problems, we have applied the proposed model to the problem of obtaining the weight of each network in a ensemble of neural networks. The results obtained are above the performance of standard methods