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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.
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.
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.
Current object recognition methods fail on object sets that include both diffuse, reflective and transparent materials, although they are very common in domestic scenarios. We show that a combination of cues from multiple sensor modalities, including specular reflectance and unavailable depth information, allows us to capture a larger subset of household objects by extending a state of the art object recognition method. This leads to a significant increase in robustness of recognition over a larger set of commonly used objects.
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 illumination is a recent technique that allows producing many diverse, optimal solutions in a map of manually defined features. To support the large amount of objective function evaluations, surrogate model assistance was recently introduced. Illumination models need to represent many more, diverse optimal regions than classical surrogate models. In this PhD thesis, we propose to decompose the sample set, decreasing model complexity, by hierarchically segmenting the training set according to their coordinates in feature space. An ensemble of diverse models can then be trained to serve as a surrogate to illumination.
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.
