A Computationally Efficient Limited Memory CMA-ES for Large Scale Optimization

We propose a computationally efficient limited memory Covariance Matrix Adaptation Evolution Strategy for large scale optimization, which we call the LM-CMA-ES. The LM-CMA-ES is a stochastic, derivative-free algorithm for numerical optimization of non-linear, non-convex optimization problems in continuous domain. Inspired by the limited memory BFGS method of Liu and Nocedal (1989), the LM-CMA-ES samples candidate solutions according to a covariance matrix reproduced from $m$ direction vectors selected during the optimization process. The decomposition of the covariance matrix into Cholesky factors allows to reduce the time and memory complexity of the sampling to $O(mn)$, where $n$ is the number of decision variables. When $n$ is large (e.g., $n$ > 1000), even relatively small values of $m$ (e.g., $m=20,30$) are sufficient to efficiently solve fully non-separable problems and to reduce the overall run-time.Comment: Genetic and Evolutionary Computation Conference (GECCO'2014) (2014

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

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

Transcutaneous treatment with vetdrop(®) sustains the adjacent cartilage in a microfracturing joint defect model in sheep

The significance of the adjacent cartilage in cartilage defect healing is not yet completely understood. Furthermore, it is unknown if the adjacent cartilage can somehow be influenced into responding after cartilage damage. The present study was undertaken to investigate whether the adjacent cartilage can be better sustained after microfracturing in a cartilage defect model in the stifle joint of sheep using a transcutaneous treatment concept (Vetdrop(®)). Carprofen and chito-oligosaccharids were added either as single components or as a mixture to a vehicle suspension consisting of a herbal carrier oil in a water-in-oil phase. This mixture was administered onto the skin with the aid of a specific applicator during 6 weeks in 28 sheep, allocated into 6 different groups, that underwent microfracturing surgery either on the left or the right medial femoral condyle. Two groups served as control and were either treated intravenously or sham treated with oxygen only. Sheep were sacrificed and their medial condyle histologically evaluated qualitatively and semi-quantitatively according to 4 different scoring systems (Mankin, ICRS, Little and O'Driscoll). The adjacent cartilage of animals of group 4 treated transcutaneously with vehicle, chito-oligosaccharids and carprofen had better histological scores compared to all the other groups (Mankin 3.3±0.8, ICRS 15.7±0.7, Little 9.0±1.4). Complete defect filling was absent from the transcutaneous treatment groups. The experiment suggests that the adjacent cartilage is susceptible to treatment and that the combination of vehicle, chitooligosaccharids and carprofen may sustain the adjacent cartilage during the recovery period

Evolving spiking networks with variable resistive memories

Neuromorphic computing is a brainlike information processing paradigm that requires adaptive learning mechanisms. A spiking neuro-evolutionary system is used for this purpose; plastic resistive memories are implemented as synapses in spiking neural networks. The evolutionary design process exploits parameter self-adaptation and allows the topology and synaptic weights to be evolved for each network in an autonomous manner. Variable resistive memories are the focus of this research; each synapse has its own conductance profile which modifies the plastic behaviour of the device and may be altered during evolution. These variable resistive networks are evaluated on a noisy robotic dynamic-reward scenario against two static resistive memories and a system containing standard connections only. The results indicate that the extra behavioural degrees of freedom available to the networks incorporating variable resistive memories enable them to outperform the comparative synapse types. © 2014 by the Massachusetts Institute of Technology

Multiobjective Calibration of a Global Biogeochemical Ocean Model Against Nutrients, Oxygen, and Oxygen Minimum Zones

Global biogeochemical ocean models rely on many parameters, which govern the interaction between individual components, and their response to the physical environment. They are often assessed/calibrated against quasi-synoptic data sets of dissolved inorganic tracers. However, a good fit to one observation might not necessarily imply a good match to another. We investigate whether two different metrics—the root-mean-square error to nutrients and oxygen and a metric measuring the overlap between simulated and observed oxygen minimum zones (OMZs)—help to constrain a global biogeochemical model in different aspects of performance. Three global model optimizations are carried out. Two single-objective optimizations target the root-mean-square metric and a sum of both metrics, respectively. We then present and explore multiobjective optimization, which results in a set of compromise solutions. Our results suggest that optimal parameters for denitrification and nitrogen fixation differ when applying different metrics. Optimization against observed OMZs leads to parameters that enhance fixed nitrogen cycling; this causes too low nitrate concentrations and a too high global pelagic denitrification rate. Optimization against nutrient and oxygen concentrations leads to different parameters and a lower global fixed nitrogen turnover; this results in a worse fit to OMZs. Multiobjective optimization resolves this antagonistic effect and provides an ensemble of parameter sets, which help to address different research questions. We finally discuss how systematic model calibration can help to improve models used for projecting climate change and its effect on fisheries and climate gas emissions