Lars Gräning

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To reduce the number of expensive fitness function evaluations in evolutionary optimization, individual-based and generation-based strategies for meta-model management (evolution control) have been proposed. In this work, four individual-based frameworks for meta-model management are investigated. A feed-forward neural network is employed to construct an(More)
— Several heuristic methods have been suggested for improving the generalization capability in neural network learning, most of which are concerned with a single-objective (SO) learning tasks. In this work, we discuss generalization improvement in multi-objective learning (MO). As a case study, we investigate the generation of neural network classifiers(More)
To reduce the number of expensive fitness function evaluations in evolutionary optimization, several individual-based and generation-based evolution control methods have been suggested. This paper compares four individual-based evolution control frameworks on three widely used test functions. Feedforward neural networks are employed for fitness estimation.(More)
Wide exploration of high-dimensional, multimodal design spaces is required for uncovering alternative solutions in the conceptual phase of design optimization tasks. We present a general framework for balancing exploration and exploitation during the course of the optimization that induces sequential exploitation of different optima in the search space by(More)
Although the integration of engineering data within the framework of product data management systems has been successful in the recent years, the holistic analysis (from a systems engineering perspective) of multidisciplinary data or data based on different representations and tools is still not realized in practice. At the same time, the application of(More)
—Due to strict CO2 emission limits, the optimal design of controllers for hybrid cars is an increasingly important topic for the automotive industry. Most current approaches to controller design rely solely on engineering knowledge. Utilizing technologies from computational intelligence is not yet common practice. In this work we evaluate how simple(More)