Quasirandom Rumor Spreading: An Experimental Analysis

We empirically analyze two versions of the well-known "randomized rumor spreading" protocol to disseminate a piece of information in networks. In the classical model, in each round each informed node informs a random neighbor. In the recently proposed quasirandom variant, each node has a (cyclic) list of its neighbors. Once informed, it starts at a random position of the list, but from then on informs its neighbors in the order of the list. While for sparse random graphs a better performance of the quasirandom model could be proven, all other results show that, independent of the structure of the lists, the same asymptotic performance guarantees hold as for the classical model. In this work, we compare the two models experimentally. This not only shows that the quasirandom model generally is faster, but also that the runtime is more concentrated around the mean. This is surprising given that much fewer random bits are used in the quasirandom process. These advantages are also observed in a lossy communication model, where each transmission does not reach its target with a certain probability, and in an asynchronous model, where nodes send at random times drawn from an exponential distribution. We also show that typically the particular structure of the lists has little influence on the efficiency.Comment: 14 pages, appeared in ALENEX'0

Online Selection of CMA-ES Variants

In the field of evolutionary computation, one of the most challenging topics is algorithm selection. Knowing which heuristics to use for which optimization problem is key to obtaining high-quality solutions. We aim to extend this research topic by taking a first step towards a selection method for adaptive CMA-ES algorithms. We build upon the theoretical work done by van Rijn \textit{et al.} [PPSN'18], in which the potential of switching between different CMA-ES variants was quantified in the context of a modular CMA-ES framework. We demonstrate in this work that their proposed approach is not very reliable, in that implementing the suggested adaptive configurations does not yield the predicted performance gains. We propose a revised approach, which results in a more robust fit between predicted and actual performance. The adaptive CMA-ES approach obtains performance gains on 18 out of 24 tested functions of the BBOB benchmark, with stable advantages of up to 23\%. An analysis of module activation indicates which modules are most crucial for the different phases of optimizing each of the 24 benchmark problems. The module activation also suggests that additional gains are possible when including the (B)IPOP modules, which we have excluded for this present work.Comment: To appear at Genetic and Evolutionary Computation Conference (GECCO'19) Appendix will be added in due tim

Bildungsgutscheine und Zertifizierung aus Sicht der Arbeitsverwaltung

Der Forschungsbericht stellt die Ergebnisse eines Befragungsprojekts zur Umsetzung der geförderten beruflichen Weiterbildung aus Sicht der Mitarbeiter der Arbeitsagenturen (SGB III) und Arbeitsgemeinschaften (SGB II) vor. Das primäre Ziel der Befragung ist es, Erkenntnisse über die Bewertung und die Umsetzung zweier Reforminstrumente in diesem Bereich zu gewinnen. Dabei handelt es sich um das im Jahre 2003 eingeführte Bildungsgutscheinverfahren und die etwas später umgesetzte Auslagerung der Zertifizierung von Bildungsträgern und Maßnahmen an private Prüfstellen. Anlass und Motivation dieser Untersuchung sind die existierenden Unterschiede in den Vergabeintensitäten der Bildungsgutscheine in den einzelnen Dienststellen. Die Ergebnisse zeigen, dass die Prozesse in der Arbeitsverwaltung grundsätzlich gut funktionieren. Es zeigen sich Unterschiede in der Bewertung und Umsetzung einzelner Reformbestandteile, die auch in den Bewertungen und Umsetzungen der Prozesse nach Rechtskreis und Verantwortungsebene der befragten Personen deutlich werden. Dabei wird der Zugang zu geförderten Weiterbildung durch Bildungsgutscheine positiv bewertet. Die Qualitätssicherung durch private Prüfstellen erhält eine neutrale Bewertung. Eine differenzierte Betrachtung nach Rechtskreis und Verantwortungseben der befragten Personen zeigt, dass dieser Reformbestandteil insbesondere von Teamleitern aus dem SGB III Bereich negativ bewertet wird.Further training is one of the most important instrument of Active Labor Market Policy (ALMP) in Germany. It was strongly affected by the Hartz-Reforms, i.e. the implementation of training vouchers for the unemployed and the introduction of a twostage certification and quality management process to ensure transparency and high quality of training courses. In this study we present the results of an onlinesurvey project that aimed to investigate the assessment and the implementation of the reform instruments from a caseworkers and team leaders point of view. Given that we observe remarkable differences in the allocation of vouchers in different employment agencies, we decide to study also the administrative part of the reform in the agencies, beside others. The results show, that the implementation of the reform instruments works well in principal. We document significant differences in the assessment of the different parts of the reform that are also obvious in differences between agencies working according the Social Code III and Social Code II. Moreover, we find differences between the assessment of caseworkers and team leaders

Contribution to the industry-specific identification and selection of a business model in machinery and equipment industry

Industry 4.0 introduces a paradigm shift that will lead to changes of business models in many e. Today, industrial companies are gradually transforming their traditional and transaction-based business models into new business models made possible by cyber-physical systems. New business models such as as-a-service or platform-based business models emerge. This change brings enormous opportunities, but also many risks for the manufacturing industry. Many companies are faced with the problem of choosing from the multitude of new business models. The business model development to be found in literature primarily follows the needs of the customer. Machinery and equipment industry is a particularly interesting sector since more than 50% of the customers in machinery and equipment industry come from the same sector. This paper develops a process model for the industry-specific selection of business models. The model includes the following questions: Which business models can be selected in the field of machinery and equipment industry? Which possible goals can be pursued with the respective business models? Which criteria are useful for deciding on the respective business model

Leveraging Benchmarking Data for Informed One-Shot Dynamic Algorithm Selection

A key challenge in the application of evolutionary algorithms in practice is the selection of an algorithm instance that best suits the problem at hand. What complicates this decision further is that different algorithms may be best suited for different stages of the optimization process. Dynamic algorithm selection and configuration are therefore well-researched topics in evolutionary computation. However, while hyper-heuristics and parameter control studies typically assume a setting in which the algorithm needs to be chosen while running the algorithms, without prior information, AutoML approaches such as hyper-parameter tuning and automated algorithm configuration assume the possibility of evaluating different configurations before making a final recommendation. In practice, however, we are often in a middle-ground between these two settings, where we need to decide on the algorithm instance before the run ("oneshot" setting), but where we have (possibly lots of) data available on which we can base an informed decision. We analyze in this work how such prior performance data can be used to infer informed dynamic algorithm selection schemes for the solution of pseudo-Boolean optimization problems. Our specific use-case considers a family of genetic algorithms.Comment: Submitted for review to GECCO'2

Quasirandom Rumor Spreading

We propose and analyze a quasirandom analogue of the classical push model for disseminating information in networks (“randomized rumor spreading”). In the classical model, in each round, each informed vertex chooses a neighbor at random and informs it, if it was not informed before. It is known that this simple protocol succeeds in spreading a rumor from one vertex to all others within O (log n ) rounds on complete graphs, hypercubes, random regular graphs, Erdős-Rényi random graphs, and Ramanujan graphs with probability 1 − o (1). In the quasirandom model, we assume that each vertex has a (cyclic) list of its neighbors. Once informed, it starts at a random position on the list, but from then on informs its neighbors in the order of the list. Surprisingly, irrespective of the orders of the lists, the above-mentioned bounds still hold. In some cases, even better bounds than for the classical model can be shown. </jats:p

Pricing of Content Services – An Empirical Investigation of Music as a Service

In the last years new concepts of digital music distribution have been developed. One of them is Music as a Service, which provides music streaming over the internet as a service - without transferring ownership for the content. This differentiates Music as a Service from Download to Own, which is used by music download platforms predominantly and is the most widely studied concept in academic research. The aim of this paper is to receive first research implications about customers’ attitudes towards MaaS. Based on an empirical survey of 132 Music as a Service users, this research explores the effects of product configurations on consumers’ utility and their willingness to pay (WTP) for premium offers. We can show that next to price, contract duration and music quality as the most important product attributes, there is a high WTP for overcoming insufficient mobile internet coverage

Automated Configuration of Genetic Algorithms by Tuning for Anytime Performance

Finding the best configuration of algorithms' hyperparameters for a given optimization problem is an important task in evolutionary computation. We compare in this work the results of four different hyperparameter tuning approaches for a family of genetic algorithms on 25 diverse pseudo-Boolean optimization problems. More precisely, we compare previously obtained results from a grid search with those obtained from three automated configuration techniques: iterated racing, mixed-integer parallel efficient global optimization, and mixed-integer evolutionary strategies. Using two different cost metrics, expected running time and the area under the empirical cumulative distribution function curve, we find that in several cases the best configurations with respect to expected running time are obtained when using the area under the empirical cumulative distribution function curve as the cost metric during the configuration process. Our results suggest that even when interested in expected running time performance, it might be preferable to use anytime performance measures for the configuration task. We also observe that tuning for expected running time is much more sensitive with respect to the budget that is allocated to the target algorithms