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Thomas H Weisswange and Christian Goerick , "Foresighted Driving Assistance Systems", 18th IEEE Intelligent Transportation Systems Conference 2015, Workshop on Interaction of Automated Vehicles with other Traffic Participants, 2015.
AbstractExperienced humans drive foresighted. They choose their behavior based on a prediction of most probable behaviors of other traffic participants and incorporate small adaptations the help avoid potential risks. The environment is up to a certain degree compliant, i.e. also reacts to the behavior of the ego vehicle. That means, pure prediction is not sufficient for true foresighted driving because a large class of hazardous events has a low probabi...
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Ran Cheng, Yaochu Jin, Kaname Narukawa , "A Multiobjective Evolutionary Algorithm using Gaussian Process based Inverse Modeling", IEEE Transactions on Evolutionary Computation, vol. 19, no. 6, pp. 838-856, 2015.
AbstractTo approximate the Pareto front, most existing multiobjective evolutionary algorithms store the non-dominated solutions found so far in the population or in an external archive during the search. Such algorithms often require a high degree of diversity of the stored solutions and only a limited number of solutions can be achieved. By contrast, model-based algorithms can alleviate the requirement on solution diversity and in principle, as m...
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Haobo Fu, Bernhard Sendhoff, Ke Tang, Xin Yao , "Robust Optimization Over Time: Problem Difficulties and Benchmark Problems", IEEE Transactions on Evolutionary Computation, vol. 9, no. 5, pp. 731-745, 2015.
AbstractThe focus of most research in evolutionary dynamic optimization has been tracking moving optimum (TMO). Yet, TMO does not capture all the characteristics of real-world dynamic optimization problems (DOPs), especially in situations where a solution’s future fitness has to be considered. To account for a solution’s future fitness explicitly, we propose to find robust solutions to DOPs, which are formulated as the robust optimization over time (ROOT...
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Lydia Fischer, Barbara Hammer, Heiko Wersing , "Efficient Rejection Strategies for Prototype-based Classification", Neurocomputing, vol. 169, pp. 334–342, 2015.
AbstractWe present simple, efficient reject options for prototype-based classification, evaluated on artificial and benchmark data sets using the example of learning vector quantization. We demonstrate that the reject options improve the accuracy in most cases, and that the performance of the proposed strategies is comparable to the optimal reject option of the Bayes classifier in cases where the latter is available. We show that the results have a compa...
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Xiaofen Lu, Stefan Menzel, Ke Tang, Xin Yao , "The Effects of Interaction Frequency in Parallel Cooperative Coevolution", The Tenth International Conference on Simulated Evolution And Learning (SEAL 2014), 2014.
AbstractCooperative coevolution (CC) employs a divide-and-conquer paradigm for tackling complex optimization problem. Its performance is influenced by many design decisions. Therefore, to beneficially use it, it is important to acquire some knowledge of the effects of different design settings. In this paper, we focus on studying the performance effects of interaction frequency in parallel CC by investigating the relationship between it and other compone...
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Stanimir Dragiev , "An Object Representation and Methods for Uncertainty Aware Shape Estimation and Grasping", An Object Representation and Methods for Uncertainty Aware Shape Estimation and Grasping, 2014.
AbstractOne of the keys to understanding intelligence is the experience of reproducing it, building it into systems we create. Robotics is the natural ground to implement, test, evaluate and realise concepts. It has already taught us that intelligence is not solely a matter of high cognition, but implies understanding of seemingly trivial everyday skills like walking, sentiment detection and interaction with the physical world. This thesis introduce...
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Marc Henniges, Richard E. Turner, Maneesh Sahani, Julian Eggert, Jörg Lücke , "Efficient Occlusive Component Analysis", Journal of Machine Learning Research, no. 15, pp. 2689−2722, 2014.
AbstractWe study unsupervised learning in a probabilistic generative model for occlusion. The model uses two types of latent variables: one indicates which objects are present in the image, and the other how they are ordered in depth. This depth order then determines how the positions and appearances of the objects present, specified in the model parameters, combine to form the image. We show that the object parameters can be learned from an unlabeled se...
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Sarah Bonnin, Thomas H Weisswange, Franz Kummert, Jens Schmüdderich , "General Behavior Prediction By A Combination Of Situation Specific Models", IEEE Transactions on Intelligent Transportation Systems, vol. 15, no. 4, pp. 1478-1488, 2014.
AbstractBefore taking a decision, a driver anticipates the future behavior of other traffic participants. However, if a driver is inattentive or overloaded he may fail to consider relevant information. This can lead to bad decisions and potentially result in an accident. A computational system designed to anticipate other traffic participants’ behaviors could assist the driver in his decision making by sending him an early warning when a risk of c...
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Giles Endicott, Markus Olhofer, Toshiyuki Arima, Toyotaka Sonoda , "A Novel Transonic Fan Swept Outlet Guide Vane Using 3D Design Optimization", Proceedings of the ASME Turbo Expo (ASME), 2014.
AbstractIn our previous work on a transonic fan swept outlet guide vane (OGV) for a small turbofan engine (GT2011-46363), we showed a novel oscillatory casing profile that leads to approximately 20% loss reduction, using a numerical design optimization method. In this paper we analyze the resulting geometry of an optimization based on a blade representation which is able to realize significantly larger surface modifications. The final optimized design di...
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Olga Smalikho and Markus Olhofer , "Co-evolution of sensory system and signal processing for optimal wing shape control", EvoStar Conference, 2014.
AbstractThis paper demonstrates the applicability of evolutionary computation methods to co-evolve a sensor morphology and a suitable control structure to optimally adjust a virtual adaptive wing structure. In opposite to approaches in which the structure of a sensor configuration is fixed early in the design stages we target the simultaneous generation of information acquisition and information processing based on an optimization of a target function. W...
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