Noisy Optimization: Convergence with a Fixed Number of Resamplings

It is known that evolution strategies in continuous domains might not converge in the presence of noise. It is also known that, under mild assumptions, and using an increasing number of resamplings, one can mitigate the effect of additive noise and recover convergence. We show new sufficient conditions for the convergence of an evolutionary algorithm with constant number of resamplings; in particular, we get fast rates (log-linear convergence) provided that the variance decreases around the optimum slightly faster than in the so-called multiplicative noise model. Keywords: Noisy optimization, evolutionary algorithm, theory.Comment: EvoStar (2014

Numerical Simulation of Magnetic Interactions in Polycrystalline YFeO3

The magnetic behavior of polycrystalline yttrium orthoferrite was studied from the experimental and theoretical points of view. Magnetization measurements up to 170 kOe were carried out on a single-phase YFeO3 sample synthesized from heterobimetallic alkoxides. The complex interplay between weak-ferromagnetic and antiferromagnetic interactions, observed in the experimental M(H) curves, was successfully simulated by locally minimizing the magnetic energy of two interacting Fe sublattices. The resulting values of exchange field (H_E = 5590 kOe), anisotropy field (H_A = 0.5 kOe) and Dzyaloshinsky-Moriya antisymmetric field (H_D = 149 kOe) are in good agreement with previous reports on this system.Comment: 26 pages, 9 figure

Analysis of Different Types of Regret in Continuous Noisy Optimization

The performance measure of an algorithm is a crucial part of its analysis. The performance can be determined by the study on the convergence rate of the algorithm in question. It is necessary to study some (hopefully convergent) sequence that will measure how "good" is the approximated optimum compared to the real optimum. The concept of Regret is widely used in the bandit literature for assessing the performance of an algorithm. The same concept is also used in the framework of optimization algorithms, sometimes under other names or without a specific name. And the numerical evaluation of convergence rate of noisy algorithms often involves approximations of regrets. We discuss here two types of approximations of Simple Regret used in practice for the evaluation of algorithms for noisy optimization. We use specific algorithms of different nature and the noisy sphere function to show the following results. The approximation of Simple Regret, termed here Approximate Simple Regret, used in some optimization testbeds, fails to estimate the Simple Regret convergence rate. We also discuss a recent new approximation of Simple Regret, that we term Robust Simple Regret, and show its advantages and disadvantages.Comment: Genetic and Evolutionary Computation Conference 2016, Jul 2016, Denver, United States. 201

Annealing schedule from population dynamics

We introduce a dynamical annealing schedule for population-based optimization algorithms with mutation. On the basis of a statistical mechanics formulation of the population dynamics, the mutation rate adapts to a value maximizing expected rewards at each time step. Thereby, the mutation rate is eliminated as a free parameter from the algorithm.Comment: 6 pages RevTeX, 4 figures PostScript; to be published in Phys. Rev.

Optimizing the Stark-decelerator beamline for the trapping of cold molecules using evolutionary strategies

We demonstrate feedback control optimization for the Stark deceleration and trapping of neutral polar molecules using evolutionary strategies. In a Stark-decelerator beamline pulsed electric fields are used to decelerate OH radicals and subsequently store them in an electrostatic trap. The efficiency of the deceleration and trapping process is determined by the exact timings of the applied electric field pulses. Automated optimization of these timings yields an increase of 40 % of the number of trapped OH radicals.Comment: 7 pages, 4 figures (RevTeX) (v2) minor corrections (v3) no changes to manuscript, but fix author list in arXiv abstrac

Analysis of the Hydrogen-rich Magnetic White Dwarfs in the SDSS

We have calculated optical spectra of hydrogen-rich (DA) white dwarfs with magnetic field strengths between 1 MG and 1000 MG for temperatures between 7000 K and 50000 K. Through a least-squares minimization scheme with an evolutionary algorithm, we have analyzed the spectra of 114 magnetic DAs from the SDSS (95 previously published plus 14 newly discovered within SDSS, and five discovered by SEGUE). Since we were limited to a single spectrum for each object we used only centered magnetic dipoles or dipoles which were shifted along the magnetic dipole axis. We also statistically investigated the distribution of magnetic-field strengths and geometries of our sample.Comment: to appear in the proceedings of the 16th European Workshop on White Dwarfs, Barcelona, 200