Rail accessibility in Germany: Changing regional disparities between 1990 and 2020

Transport accessibility is an important location factor for households and firms. In the last few decades, technological and social developments have contributed to a reinvigorated role of passenger transport. However, rail accessibility is unevenly distributed in space. The introduction of high-speed rail has furthermore promoted a polarisation of accessibility between metropolises and peripheral areas in some European countries. In this article we analyse the development of rail accessibility at the regional level in Germany between 1990 and 2020 for 266 functional city-regions. Our results show two different facets: The number of regions that are directly connected to one another has decreased, but at the same time the spatial disparities of accessibility have decreased, albeit to a small extent. This development was strongest in East Germany after German reunification and thus largely a consequence of the renovation of the conventional rail infrastructure, not high-speed rail. Nevertheless, it can be concluded that the introduction of high-speed traffic in Germany did not lead to an increase in accessibility disparities. Instead, the accessibility effects of high-speed rail in Germany seem to break the traditional dichotomy between core and periphery.Verkehrliche Erreichbarkeit stellt einen wichtigen Standortfaktor für Haushalte und Unternehmen dar. In den letzten Jahrzehnten haben technologische und soziale Entwicklungen zu einer neuen Attraktivität des Schienenpersonenverkehrs beigetragen. Die Erreichbarkeit über den Schienenverkehr fällt jedoch räumlich sehr unterschiedlich aus. Die Einführung des Hochgeschwindigkeitsverkehrs hat zudem in einigen europäischen Ländern eine Polarisierung der Erreichbarkeit zwischen Metropolen und peripheren Räumen befördert. In diesem Beitrag analysieren wir die Entwicklung der Bahnerreichbarkeit auf regionaler Ebene in Deutschland zwischen 1990 und 2020 für 266 funktionale Stadtregionen. Unsere Ergebnisse zeigen zwei unterschiedliche Facetten: Die Zahl der direkt miteinander verbundenen Regionen hat sich verringert, aber zugleich zeigt sich für die Erreichbarkeit der Bevölkerung eine Abschwächung der räumlichen Disparitäten, wenn auch in geringem Maße. Diese Entwicklung war in Ostdeutschland nach der deutschen Wiedervereinigung am stärksten und damit weitgehend eine Folge der Sanierung der konventionellen Schieneninfrastruktur, nicht des Hochgeschwindigkeitsverkehrs. Dennoch kann der Schluss gezogen werden, dass seine Einführung in Deutschland nicht zur Erhöhung von Erreichbarkeitsdisparitäten geführt hat. Stattdessen scheinen die Erreichbarkeitswirkungen des Hochgeschwindigkeitsverkehrs in Deutschland die traditionelle Dichotomie zwischen Kern und Peripherie zu durchbrechen

Multiobjective exploration of the StarCraft map space

This paper presents a search-based method for generating maps for the popular real-time strategy (RTS) game StarCraft. We devise a representation of StarCraft maps suitable for evolutionary search, along with a set of fitness functions based on predicted entertainment value of those maps, as derived from theories of player experience. A multiobjective evolutionary algorithm is then used to evolve complete Star- Craft maps based on the representation and selected fitness functions. The output of this algorithm is a Pareto front approximation visualizing the tradeoff between the several fitness functions used, and where each point on the front represents a viable map. We argue that this method is useful for both automatic and machine-assisted map generation, and in particular that the Pareto fronts are excellent design support tools for human map designers.This research was supported in part by the Danish Research Agency, Ministry of Science, Technology and Innovation; project name: Adaptive Game Content Creation using Computational Intelligence (AGameComIn); project number: 274-09-0083.peer-reviewe

Summary of the First GECCO Workshop on Theoretical Aspects of Evolutionary Multiobjective Optimization

Recently, the first Workshop on Theoretical Aspects of Evolutionary Multiobjective Optimization (EMO) has been taking place on July 8, 2010 in Portland, OR, USA, co-located with the Genetic and Evolutionary Computation Conference (GECCO). Besides a brief presentation of the workshop talks, we mainly focus here on summarizing the results of the discussions. In particular, we present a list of open problems that has been compiled before and during the workshop by the participants. We hope that this overview of the latest forefront of theoretical research in the field of EMO will inspire future work and foster collaborations among the members of the EMO community

Faster S-Metric Calculation by Considering Dominated Hypervolume as Klee’s Measure Problem

The dominated hypervolume (or S-metric) is a commonly accepted quality measure for comparing approximations of Pareto fronts generated by multi-objective optimizers. Since optimizers exist, namely evolutionary algorithms, that use the S-metric internally several times per iteration, a faster determination of the S-metric value is of essential importance. This paper describes how to consider the S-metric as a special case of a more general geometrical problem called Klee’s measure problem (KMP). For KMP an algorithm exists with run time O(n logn + n d/2 log n), for n points of d ≥ 3 dimensions. This complex algorithm is adapted to the special case of calculating the S-metric. Conceptual simplifications of the implementation are concerned that save on a factor of O(logn) and establish an upper bound of O(n logn + n d/2) for the S-metric calculation, improving the previously known bound of O(n d−1)

Multi-objective Optimisation Using S-metric Selection: Application to three-dimensional Solution Spaces

The S-metric or hypervolume measure is a distinguished quality measure for solution sets in Pareto optimisation. Once the aim to reach a high S-metric value is appointed, it seems to be promising to directly incorporate it in the optimisation algorithm. This idea has been implemented in the SMS-EMOA, an evolutionary multi-objective optimisation algorithm (EMOA) using the hypervolume measure within its selection operator. Solutions are rated according to their contribution to the dominated hypervolume of the current population. Up to now, the SMS-EMOA has only been applied to functions with two objectives. The work at hand extends these studies, by surveying the behaviour of the algorithm on three-objective problems. Additionally, a new efficient algorithm for the computation of the contributions to the dominated hypervolume in threedimensional solution spaces is presented. Different variants of selection operators are proposed. Among these, a new one is presented that rates a solution concerning the number of solutions dominating it. So, solutions in less explored regions are preferred. This rating is an efficient alternative to the S-metric criterion whenever a selection among dominated solutions has to be made. Comparative studies on standard benchmark problems show that the SMS-EMOA clearly outperforms other well established EMOA. First results on a challenging realworld problem have been obtained, namely the multipoint design of an airfoil involving three objectives and nonlinear constraints. Not only a clear improvement of the baseline design, but a good coverage of the Pareto front with a small, limited number of points has been achieved

An EMO Algorithm Using the Hypervolume Measure as Selection Criterion

The hypervolume measure is one of the most frequently applied measures for comparing the results of evolutionary multiobjective optimization algorithms (EMOA). The idea to use this measure for selection is self-evident. A steady-state EMOA will be devised, that combines concepts of non-dominated sorting with a selection operator based on the hypervolume measure. The algorithm computes a well distributed set of solutions with bounded size thereby focussing on interesting regions of the Pareto front(s). By means of standard benchmark problems the algorithm will be compared to other well established EMOA. The results show that our new algorithm achieves good convergence to the Pareto front and outperforms standard methods in the hypervolume covered

Pareto-, Aggregation-, and Indicator-based Methods in Many-objective Optimization

Research within the area of Evolutionary Multi-objective Optimization (EMO) focused on two- and three-dimensional objective functions, so far. Most algorithms have been developed for and tested on this limited application area. To broaden the insight in the behavior of EMO algorithms (EMOA) in higher dimensional objective spaces, a comprehensive benchmarking is presented, featuring several state-of-the-art EMOA, as well as an aggregative approach and a restart strategy on established scalable test problems with three to six objectives. It is demonstrated why the performance of well-established EMOA (NSGA-II, SPEA2) rapidly degradates with increasing dimension. Newer EMOA like ε-MOEA, MSOPS, IBEA and SMS-EMOA cope very well with high-dimensional objective spaces. Their specific advantages and drawbacks are illustrated, thus giving valuable hints for practitioners which EMOA to choose depending on the optimization scenario. Additionally, a new method for the generation of weight vectors usable in aggregation methods is presented