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An evolving strategy for a multi-stage placement of charging stations for electrical cars is developed. Both an incremental as well as a decremental placement decomposition are evaluated on this Maximum Covering Location Problem. We show that an incremental Genetic Algorithm benefits from problem decomposition effects of having multiple stages and shows greedy behaviour.

Aufgrund eines nahezu gleichlautenden Beschlusses des Kreistages im Rhein-Sieg-Kreis (RSK) und des Hauptausschusses der Stadt Bonn im Jahr 2011 wurden die jeweiligen Verwaltungen beauftragt, gemeinsam mit den Energieversorgern der Region ein Starthilfekonzept Elektromobilität zu entwickeln. In Folge dieses Beschlusses konstituierte sich Ende 2011 ein Arbeitskreis, der aus den Verwaltungen des Rhein-Sieg-Kreises und der Stadt Bonn, den Energieversorgern SWB Energie und Wasser, der Rhenag, den Stadtwerken Troisdorf, der Rheinenergie und den RWE besteht. Die inhaltlichen Schwerpunkte, die inzwischen in drei Arbeitskreisen behandelt werden, umfassen den Ausbau der Ladeinfrastruktur, die Öffentlichkeitsarbeit und die Bereitstellung von Strom aus regenerativen Quellen durch den Zubau entsprechender Anlagen in der Region. Während Maßnahmen zur Öffentlichkeitsarbeit und die Bereitstellung Grünen Stroms aus den Arbeitskreisen direkt bearbeitet und bewegt werden, ist dies aufgrund der Komplexität des Themas und der zahlreichen Einflussgrößen beim Ausbau der Ladeinfrastruktur nicht möglich. Daraus entstand die Überlegung einer Kooperation mit der Hochschule Bonn-Rhein-Sieg.

An iterative computer-aided ideation procedure is introduced, building on recent quality-diversity algorithms, which search for diverse as well as high-performing solutions. Dimensionality reduction is used to define a similarity space, in which solutions are clustered into classes. These classes are represented by prototypes, which are presented to the user for selection. In the next iteration, quality-diversity focuses on searching within the selected class. A quantitative analysis is performed on a 2D airfoil, and a more complex 3D side view mirror domain shows how computer-aided ideation can help to enhance engineers' intuition while allowing their design decisions to influence the design process.

Evolutionary computation and genetic algorithms (GAs) in particular have been applied very successfully to many real world application problems. However, the success or failure of applying Genetic Algorithms is highly dependent on how a problem is represented. Additionally, the number of free parameters makes applying these methods a science of its own, presenting a huge barrier to entry for beginners. This tutorial will give a summary on various representational aspects, discuss parametrization and their influence on the dynamics of GAs.

Maximal covering location problems have efficiently been solved using evolutionary computation. The multi-stage placement of charging stations for electric cars is an instance of this problem which is addressed in this study. It is particularly challenging, because a final solution is constructed in multiple steps, stations cannot be relocated easily and intermediate solutions should be optimal with respect to certain objectives.

Neuroevolution methods evolve the weights of a neural network, and in some cases the topology, but little work has been done to analyze the effect of evolving the activation functions of individual nodes on network size, an important factor when training networks with a small number of samples. In this work we extend the neuroevolution algorithm NEAT to evolve the activation function of neurons in addition to the topology and weights of the network. The size and performance of networks produced using NEAT with uniform activation in all nodes, or homogenous networks, is compared to networks which contain a mixture of activation functions, or heterogenous networks. For a number of regression and classification benchmarks it is shown that, (1) qualitatively different activation functions lead to different results in homogeneous networks, (2) the heterogeneous version of NEAT is able to select well performing activation functions, (3) the produced heterogeneous networks are significantly smaller than homogeneous networks.

Theoretische Informatik
(2002)

Eine anschauliche Einführung in die klassischen Themenbereiche der Theoretischen Informatik für Studierende der Informatik im Haupt- und Nebenfach. Die Autoren wählen einen Ansatz, der durch zahlreiche ausgearbeitete Beispiele auch LeserInnen mit nur elementaren Mathematikkenntnissen den Zugang zu Berechenbarkeit, Komplexitätstheorie und formalen Sprachen ermöglicht. Die mathematischen Konzepte werden sowohl formal eingeführt als auch informell erläutert und durch grafische Darstellungen veranschaulicht. Das Buch umfasst den Lehrstoff einführender Vorlesungen in die Theoretische Informatik und bietet zahlreiche Übungsaufgaben zu jedem Kapitel an.

Analyzing training performance in sport is usually based on standardized test protocols and needs laboratory equipment, e.g., for measuring blood lactate concentration or other physiological body parameters. Avoiding special equipment and standardized test protocols, we show that it is possible to reach a quality of performance simulation comparable to the results of laboratory studies using training models with nothing but training data. For this purpose, we introduce a fitting concept for a performance model that takes the peculiarities of using training data for the task of performance diagnostics into account. With a specific way of data preprocessing, accuracy of laboratory studies can be achieved for about 50% of the tested subjects, while lower correlation of the other 50% can be explained.

With the increasing average age of the population in many developed countries, afflictions like cardiovascular diseases have also increased. Exercising has a proven therapeutic effect on the cardiovascular system and can counteract this development. To avoid overstrain, determining an optimal training dose is crucial. In previous research, heart rate has been shown to be a good measure for cardiovascular behavior. Hence, prediction of the heart rate from work load information is an essential part in models used for training control. Most heart-rate-based models are described in the context of specific scenarios, and have been evaluated on unique datasets only. In this paper, we conduct a joint evaluation of existing approaches to model the cardiovascular system under a certain strain, and compare their predictive performance. For this purpose, we investigated some analytical models as well as some machine learning approaches in two scenarios: prediction over a certain time horizon into the future, and estimation of the relation between work load and heart rate over a whole training session.

During exercise, heart rate has proven to be a good measure in planning workouts. It is not only simple to measure but also well understood and has been used for many years for workout planning. To use heart rate to control physical exercise, a model which predicts future heart rate dependent on a given strain can be utilized. In this paper, we present a mathematical model based on convolution for predicting the heart rate response to strain with four physiologically explainable parameters. This model is based on the general idea of the Fitness-Fatigue model for performance analysis, but is revised here for heart rate analysis. Comparisons show that the Convolution model can compete with other known heart rate models. Furthermore, this new model can be improved by reducing the number of parameters. The remaining parameter seems to be a promising indicator of the actual subject’s fitness.

The MAP-Elites algorithm produces a set of high-performing solutions that vary according to features defined by the user. This technique has the potential to be a powerful tool for design space exploration, but is limited by the need for numerous evaluations. The Surrogate-Assisted Illumination algorithm (SAIL), introduced here, integrates approximative models and intelligent sampling of the objective function to minimize the number of evaluations required by MAP-Elites.
The ability of SAIL to efficiently produce both accurate models and diverse high performing solutions is illustrated on a 2D airfoil design problem. The search space is divided into bins, each holding a design with a different combination of features. In each bin SAIL produces a better performing solution than MAP-Elites, and requires several orders of magnitude fewer evaluations. The CMA-ES algorithm was used to produce an optimal design in each bin: with the same number of evaluations required by CMA-ES to find a near-optimal solution in a single bin, SAIL finds solutions of similar quality in every bin.

Training models have been proposed to model the effect of physical strain on fitness. In this work we explore their use not only for analysis but also to generate training plans to achieve a given fitness goal. These plans have to include side constraints such as, e.g., maximal training loads. Therefore plan generation can be treated as a constraint satisfaction problem and thus can be solved by classical CSP solvers. We show that evolutionary algorithms such as differential evolution or CMA-ES produce comparable results while allowing for more flexibility and requiring less computational resources. Due to this flexibility, it is possible to include well known principles of training science during plan generation, resulting in reasonable training plans.

Microcontroller-based sensor systems offer great opportunities for the implementation of safety features for potentially dangerous machinery. However, in general they are difficult to assess with regard to their reliability and failure rate. This paper describes the safety assessment of hardware and software of a new and innovative sensor system. The hardware is assessed by standardized methods according to norm EN ISO 13849-1, while the use of model checking is presented as an approach to solve the problem of validating the software.

The Fitness-Fatigue model (Calvert et al. 1976) is widely used for performance analysis. This antagonistic model is based on a fitness-term, a fatigue-term, and an initial basic level of performance. Instead of generic parameter values, individualizing the model needs a fitting of parameters. With fitted parameters, the model adapts to account for individual responses to strain. Even though in most cases fitting of recorded training data shows useful results, without modification the model cannot be simply used for prediction.

A comprehensive analysis of cardiovascular control (CVC) patterns with multiple subjects is presented. It became feasible by recent methodological advances. Simple computer models were generated automatically, reproducing only factors of the true model that are relevant to the focus if investigation. These models?named aspect-models?could in turn be used in model individualization, thus reducing the necessary computational amount. The achieved speedup by a factor of more than three thousand and the high numerical stability of the resulting method allows the unsupervised identification of a large body of experimental data. The analysis of tilt table experiments of 18 subjects revealed a remarkable variety of reaction patterns. Closer examination yielded different classes of subjects. Two main groups corresponding to basic types of CVC were observed. Three outliers could be assigned to the specific situation of some subjects.

A concept called model individualization is presented. It is used to modify computer based models to reproduce observed individual behavior. During D2-, MIR97- and Neurolab-missions tilt-table and LBNP-experiments were carried out. Physiological data describing the cardiovascular reactions of the astronauts were recorded. The appropriateness of the rheoretical principles is demonstrated with MIR97 tilt-table experiments. Finally the resulting individualized model is investigated to propose hypotheses on probable alterations in the cardiovascular system induced by microgravity.

Surrogate-assistance approaches have long been used in computationally expensive domains to improve the data-efficiency of optimization algorithms. Neuroevolution, however, has so far resisted the application of these techniques because it requires the surrogate model to make fitness predictions based on variable topologies, instead of a vector of parameters. Our main insight is that we can sidestep this problem by using kernel-based surrogate models, which require only the definition of a distance measure between individuals. Our second insight is that the well-established Neuroevolution of Augmenting Topologies (NEAT) algorithm provides a computationally efficient distance measure between dissimilar networks in the form of "compatibility distance", initially designed to maintain topological diversity. Combining these two ideas, we introduce a surrogate-assisted neuroevolution algorithm that combines NEAT and a surrogate model built using a compatibility distance kernel. We demonstrate the data-efficiency of this new algorithm on the low dimensional cart-pole swing-up problem, as well as the higher dimensional half-cheetah running task. In both tasks the surrogate-assisted variant achieves the same or better results with several times fewer function evaluations as the original NEAT.

The MAP-Elites algorithm produces a set of high-performing solutions that vary according to features defined by the user. This technique to 'illuminate' the problem space through the lens of chosen features has the potential to be a powerful tool for exploring design spaces, but is limited by the need for numerous evaluations. The Surrogate-Assisted Illumination (SAIL) algorithm, introduced here, integrates approximative models and intelligent sampling of the objective function to minimize the number of evaluations required by MAP-Elites.
The ability of SAIL to efficiently produce both accurate models and diverse high-performing solutions is illustrated on a 2D airfoil design problem. The search space is divided into bins, each holding a design with a different combination of features. In each bin SAIL produces a better performing solution than MAP-Elites, and requires several orders of magnitude fewer evaluations. The CMA-ES algorithm was used to produce an optimal design in each bin: with the same number of evaluations required by CMA-ES to find a near-optimal solution in a single bin, SAIL finds solutions of similar quality in every bin.

The Fitness Fatigue model is often used for performance analysis. It uses an initial basic level of performance and two antagonistic terms: a fitness-term and a fatigue-term. By fitting the models parameters, we adapt the model to the subject’s individual physical response to strain. Even though in most cases fitting of recorded training data shows useful results, without modification the model cannot be simply used for prediction.

Behaviour-based robotics (cf. Brooks [2]) has mainly been applied to the domain of autonomous systems and mobile robots. In this paper we show how this approach to robot programming can be used to design a flexible and robust controller for a five degrees of freedom (DOF) robot arm. The implementation of the robot controller to be presented features the sensor and motor patterns necessary to tackle a problem we consider to be hard to solve for traditional controllers. These sensor and motor patterns are linked together forming various behaviours. The global control structure based on Brooks' subsumption architecture will be outlined. It coordinates the individual behaviours into goal-directed behaviour of the robot without the necessity to program this emerging global behaviour explicitly and in advance. To conclude, some shortcomings of the current implementation are discussed and future work, especially in the field of reinforcement learning of individual behaviours, is sketched.

This paper describes the development of a Pedelec controller whose performance level (PL) conforms to European standard on safety of machinery [9] and whose soft- ware is verified to conform to EPAC standard [6] by means of a software verification technique called model checking. In compliance with the standard [9] the hardware needs to implement the required properties corresponding to categories “C” and “D”. The latter is used if the breaks are not able to bring the velomobile with a broken motor controller to a full stop. Therefore the controller needs to implement a test unit, which verifies the functionality of the components and, in case of an emergency, shuts the whole hardware down to prevent injuries of the cyclist. The MTTFd can be measured through a failure graph, which is the result of a FMEA analysis, and can be used to proof that the Pedelec controller meets the regulations of the system specification. The analysis of the system in compliance with [9] usually treats the software as a black box thus ignoring its inner workings and validating its correctness by means of testing. In this paper we present a temporal logic specification according to [6], based on which the software for the Pedelec controller is implemented, and verify instead of only testing its functionality. By means of model checking [1] we proof that the software fulfills all requirements which are regulated by its specification.

A detailed analysis of autonomic cardiovascular control (ACVC) may provide a key to a better understanding of the mechanisms underlying postflight orthostatic hypotension. The central substrate of human ACVC is not directly accessible to measurements and observation in space research. Modelling--supporting inference and physiological reasoning--is a valuable tool to disclose its involvement We are currently determining the suitability of artificial neural networks (ANN's) as a model of the central substrate of ACVC. Having conducted a number of experiments with simulated tilt test data to clarify the choice of input coding and of architectural biases in network training we will now report on the approximation of data obtained from human subjects during preparation of the German MIR'97 and D-2 missions.