Improved Runtime Bounds for the Univariate Marginal Distribution Algorithm via Anti-Concentration

Unlike traditional evolutionary algorithms which produce offspring via genetic operators, Estimation of Distribution Algorithms (EDAs) sample solutions from probabilistic models which are learned from selected individuals. It is hoped that EDAs may improve optimisation performance on epistatic fitness landscapes by learning variable interactions. However, hardly any rigorous results are available to support claims about the performance of EDAs, even for fitness functions without epistasis. The expected runtime of the Univariate Marginal Distribution Algorithm (UMDA) on OneMax was recently shown to be in $\mathcal{O}\left(n\lambda\log \lambda\right)$ by Dang and Lehre (GECCO 2015). Later, Krejca and Witt (FOGA 2017) proved the lower bound $\Omega\left(\lambda\sqrt{n}+n\log n\right)$ via an involved drift analysis. We prove a $\mathcal{O}\left(n\lambda\right)$ bound, given some restrictions on the population size. This implies the tight bound $\Theta\left(n\log n\right)$ when $\lambda=\mathcal{O}\left(\log n\right)$, matching the runtime of classical EAs. Our analysis uses the level-based theorem and anti-concentration properties of the Poisson-Binomial distribution. We expect that these generic methods will facilitate further analysis of EDAs.Comment: 19 pages, 1 figur

Importance mixing: Improving sample reuse in evolutionary policy search methods

Deep neuroevolution, that is evolutionary policy search methods based on deep neural networks, have recently emerged as a competitor to deep reinforcement learning algorithms due to their better parallelization capabilities. However, these methods still suffer from a far worse sample efficiency. In this paper we investigate whether a mechanism known as "importance mixing" can significantly improve their sample efficiency. We provide a didactic presentation of importance mixing and we explain how it can be extended to reuse more samples. Then, from an empirical comparison based on a simple benchmark, we show that, though it actually provides better sample efficiency, it is still far from the sample efficiency of deep reinforcement learning, though it is more stable

Upper Bounds on the Runtime of the Univariate Marginal Distribution Algorithm on OneMax

A runtime analysis of the Univariate Marginal Distribution Algorithm (UMDA) is presented on the OneMax function for wide ranges of its parameters $\mu$ and $\lambda$. If $\mu\ge c\log n$ for some constant $c>0$ and $\lambda=(1+\Theta(1))\mu$, a general bound $O(\mu n)$ on the expected runtime is obtained. This bound crucially assumes that all marginal probabilities of the algorithm are confined to the interval $[1/n,1-1/n]$. If $\mu\ge c' \sqrt{n}\log n$ for a constant $c'>0$ and $\lambda=(1+\Theta(1))\mu$, the behavior of the algorithm changes and the bound on the expected runtime becomes $O(\mu\sqrt{n})$, which typically even holds if the borders on the marginal probabilities are omitted. The results supplement the recently derived lower bound $\Omega(\mu\sqrt{n}+n\log n)$ by Krejca and Witt (FOGA 2017) and turn out as tight for the two very different values $\mu=c\log n$ and $\mu=c'\sqrt{n}\log n$. They also improve the previously best known upper bound $O(n\log n\log\log n)$ by Dang and Lehre (GECCO 2015).Comment: Version 4: added illustrations and experiments; improved presentation in Section 2.2; to appear in Algorithmica; the final publication is available at Springer via http://dx.doi.org/10.1007/s00453-018-0463-

Elastic Business Process Management: State of the Art and Open Challenges for BPM in the Cloud

With the advent of cloud computing, organizations are nowadays able to react rapidly to changing demands for computational resources. Not only individual applications can be hosted on virtual cloud infrastructures, but also complete business processes. This allows the realization of so-called elastic processes, i.e., processes which are carried out using elastic cloud resources. Despite the manifold benefits of elastic processes, there is still a lack of solutions supporting them. In this paper, we identify the state of the art of elastic Business Process Management with a focus on infrastructural challenges. We conceptualize an architecture for an elastic Business Process Management System and discuss existing work on scheduling, resource allocation, monitoring, decentralized coordination, and state management for elastic processes. Furthermore, we present two representative elastic Business Process Management Systems which are intended to counter these challenges. Based on our findings, we identify open issues and outline possible research directions for the realization of elastic processes and elastic Business Process Management.Comment: Please cite as: S. Schulte, C. Janiesch, S. Venugopal, I. Weber, and P. Hoenisch (2015). Elastic Business Process Management: State of the Art and Open Challenges for BPM in the Cloud. Future Generation Computer Systems, Volume NN, Number N, NN-NN., http://dx.doi.org/10.1016/j.future.2014.09.00

GOES-R Dual Isolation

The Geostationary Operational Environmental Satellite-R Series (GOES-R) is the first of the next generation geostationary weather satellites, scheduled for delivery in late 2015. GOES-R represents a quantum increase in Earth and solar weather observation capabilities, with 4 times the resolution, 5 times the observation rate, and 3 times the number of spectral bands for Earth observations. With the improved resolution, comes the instrument suite's increased sensitive to disturbances over a broad spectrum 0-512 Hz. Sources of disturbance include reaction wheels, thruster firings for station keeping and momentum management, gimbal motion, and internal instrument disturbances. To minimize the impact of these disturbances, the baseline design includes an Earth Pointed Platform (EPP), a stiff optical bench to which the two nadir pointed instruments are collocated together with the Guidance Navigation & Control (GN&C) star trackers and Inertial Measurement Units (IMUs). The EPP is passively isolated from the spacecraft bus with Honeywell D-Strut isolators providing attenuation for frequencies above approximately 5 Hz in all six degrees-of-freedom. A change in Reaction Wheel Assembly (RWA) vendors occurred very late in the program. To reduce the risk of RWA disturbances impacting performance, a secondary passive isolation system manufactured by Moog CSA Engineering was incorporated under each of the six 160 Nms RWAs, tuned to provide attenuation at frequencies above approximately 50 Hz. Integrated wheel and isolator testing was performed on a Kistler table at NASA Goddard Space Flight Center. High fidelity simulations were conducted to evaluate jitter performance for four topologies: 1) hard mounted no isolation, 2) EPP isolation only, 2) RWA isolation only, and 4) dual isolation. Simulation results demonstrate excellent performance relative to the pointing stability requirements, with dual isolated Line of Sight (LOS) jitter less than 1 micron rad

An approach to facilitate problem solving: Individualizing the problem proposition

This paper addresses one of the many facets of the problem-solving activity: the challenge inherent in the problem proposition. We have identified the problem proposition as a core element in obtaining efficient problem solving. The Educational Dimension Portfolio, EDP, is our proposal for individualizing the problem proposition. This paper presents EDP's characteristics and implications through testing the results of 491 IESE Business School executives from the European Union (EU) and Latin America (LA). We enumerate five working hypotheses and show their results. We also propose an Educational Delivery Approach (EDA) to help managers become manager-educators. We present the Socratic educational process, the apprenticeship process and the providing alternatives process as a guide to become a manager-educator.problem solving; problem proposition; operations management; manager-educator;

Improvement and evaluation of the mesoscale meteorological model MM5 for air-quality applications in Southern California and the San Joaquin Valley: Final Report

The objective of the Penn State University (PSU) part of the study was to investigate the MM5's ability to simulate wintertime fog in the San Joaquin Valley (SJV) and summertime sea breeze flows in the South Coast Air Basin (SoCAB). For the SJV work the MM5 was configured with four nested grid and an advanced turbulence sub-model. Applied to the event of 7-12 December 1995, observed during the IMS-95 program, the model's innermost domain used 40 vertical layers and a 4-km mesh. Several experiments were performed to improve the turbulence sub-model for saturated conditions and to provide more accurate initial conditions for soil temperature and moisture. Results showed the MM5 correctly predicted the type of visibility obscuration (fog, haze, status or clear) in 14 out of the 18 events. For depth was estimated by the MM5 with a mean absolute error of only 92 m and a mean error of -41 m. Mean errors for both the surface temperature and dew point were within +1C, while the mean absolute errors were ~1.5-2.0 C. As a consequence, the mean error for dew-point depression is very small. Thus, the MM5 was shown to simulate fog and haze in the SJV with considerable accuracy. Extensions of the turbulence sub-model to include saturation effects and the specification of accurate soil temperature and moisture were important for simulating fog characteristics in the case. Additionally, MM5 was able to simulate the light and variable winds in the Sacramento and San Joaquin Valleys that prevailed during this event. Moreover, the winds responded quite well to the slowly changing synoptic-scale weather, as well, as confirmed by the observations. the objective for the San Jose State University (SJSU) work included use of SCOS97 data and MM5 simulations to understand meteorological factors in the formation of high ozone concentrations during 4-7 August 1997. Meteorological data for the case study included observations at 110 SCOS97 surface sites and upper air measurements from 12 rawinsonde and 26 RWP/RASS profilers. the MM5 version contained the PSU Marine Boundary Layer Initialization (MBLI) scheme, quadruple nested grids (horizontal resolutions of 135, 45, 15, and 5 km), 30 vertical layers, minimum sigma level of 46 m, USGS global land-use, GDAS global gridded model analyses and SSTs, analysis nudging, observational nudging, force-restore surface temperature, 1.5 order TKE, one-way continuous nesting, and a MAPS statistical evaluation. Analysis showed the ozone episode resulting from a unique combination of large-scale upper level synoptic forcings that included a weak local coastal 700 mb anticyclone. Its movement around SoCAB rotated the upper level synoptic background flow from its normal westerly onshore direction to a less common offshore easterly flow during the nighttime period preceding the episode. The resulting easterly upper level synoptic background winds influenced surface flow direction at inland sites, so that a surface frontal convergence zone resulted where the easterly flow met the westerly onshore sea breeze flow. The maximum inland penetration of the convergence zone was about to the San Gabriel Mountain peaks, the location of daytime maximum ozone-episode concentrations. The current MM5 simulations reproduced the main qualitative features of the evolution of the diurnal sea breeze cycle in the SoCAB with reasonable accuracy. The position of the sea breeze front during its daytime inland penetration and nighttime retreat could be determined from the simulated wind fields. the accuracy of predicted MM5 surface winds and temperatures over SoCAB were improved by the modifications of its deep-soil temperatures, interpolation of predicted temperatures and winds to SCOS97 observational levels, use of updated urban land-use patterns, and use of corrected input values for ocean and urban surface roughness parameter values.Prepared for the California Air Resources Board and California Environmental Protection AgencySJSU Foundation Subcontract no. 22-1505-7384Approved for public release; distribution is unlimited