Open Access

This paper derives a population sizing model for standard Evolution Strategies (ES) in highly multimodal fitness landscapes with exponentially many local optima. The Rastrigin, Bohachevsky, and Ackley test functions are considered. Due to the highly non-convex structure of these functions a detailed analytical description of the behavior of the ES is a challenge. Therefore, a model is derived that simplifies the complex structure of the functions under consideration. The main idea of this model is the interpretation of local landscape oscillations as frozen noise. This allows for an estimation of the success probability of the ES converging to the global optimum and in turn an estimation of the population size required. It is shown that the population size scales usually sublinearly with the search space dimension N. For the Rastrigin and Bohachevsky function, the population size scales with O(√N ln(N)). As for Ackley, the scaling behavior depends strongly on the initial values. If the algorithm starts in a certain vicinity of the global optimizer, the dependence on the dimension N is rather weak. However, if the initial value exceeds a certain distance R to the optimizer, the population size scales exponentially with R.

«Big Data» haben ein großes Potenzial, um die Wertschöpfung effizienter zu gestalten oder um Innovationen hervorzubringen. Daten werden oft an der Schnittstelle zwischen mehreren Akteuren in Business-to-Business-Ökosystemen generiert und sie müssen zwischen den Akteuren geteilt werden. Unternehmen tun sich jedoch schwer damit, Daten in Werte zu transferieren und die Daten im Ökosystem zu teilen. Ursächlich sind weniger technische Gründe als organisationale Rahmenbedingungen. Der Beitrag identifiziert fünf Perspektiven, die Hürden und Voraussetzungen in diesem Prozess darstellen: (1) eine datengetriebene Organisationskultur, (2) Vertrauen zwischen den Akteuren, (3) die Konkretisierung des Wertes von Daten, (4) Datensicherheit und (5) rechtliche und Governance-Aspekte. Eine Fallstudie eines typischen Daten-Ökosystems um ein produzierendes KMU konkretisiert diese Voraussetzungen und Hürden. Es zeigt sich, dass sich Unternehmen, die Daten im Ökosystem teilen möchten, ganzheitlich verändern müssen.

Why do some countries assign a major role to wind energy in decarbonizing their electricity systems, while others are much less committed to this technology? We argue that processes of (de-)legitimation, driven by discourse coalitions who strategically employ certain storylines in public debates, provide part of the answer. To illustrate our approach, we comparatively investigate public discourses surrounding wind energy in Austria and Switzerland, two countries that differ strongly in wind energy deployment. By combining a qualitative content analysis and a discourse network analysis of 808 newspaper articles published 2010–2020, we identify four distinct sets of storylines used to either delegitimize or legitimize the technology. Our study indicates that low deployment rates in Switzerland can be related to the prominence of delegitimizing storylines in the public discourse, which result in a rather low socio-political acceptance of wind energy. In Austria, by contrast, there is more consistent support for wind energy by discourse coalitions using a broad set of legitimizing storylines. By bridging the related but separate literatures of technology legitimacy and social acceptance, our study contributes to a better understanding of socio-political conflict and divergence in low-carbon technological pathways.

This paper investigates restart strategies for algorithms whose success depends on an algorithmic parameter λ. It is assumed that there exists a unique unknown optimal λ. After each restart λ is increased. The main question is whether there is an optimal strategy for choosing λ after each restart. To this end, possible restart strategies are classified into parameter-dependent strategy types. A loss function is introduced, that measures the wasted computational costs compared to the optimal strategy. One criterion that a viable restart strategy must satisfy is that the loss relative to the optimal λ is bounded. Experimental evidence demonstrates that this is not the case for all strategy types. However, for a specific strategy type, where the parameter λ is increased multiplicatively with an increasing constant ρ, the relative loss function has an upper bound. It will be shown, that for this strategy type there is an optimal choice for the parameter ρ that is independent of the optimal λ.