Titlebook: High Performance Computing in Science and Engineering ’02; Transactions of the Egon Krause,Willi J?ger Conference proceedings 2003 Springe

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W. G. Schmidt,P. H. Hahn,F. Bechstedtdisease and to provide better diagnoses, treatment, and even prevention. The non-linear interactions among multiple genetic factors play an important role in finding those genetic variations, but have not always been taken fully into account. This is due to the fact that searching combinations of in

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Zhongbing Huang,Werner Hanke,Enrico Arrigoniate Metaheuristic Rule Set Learners (MRSLs), a class of algorithms that utilize metaheuristics such as Genetic Algorithms to solve learning tasks: The commonly-used predictive performance-based metrics such as mean absolute error do not capture most users’ actual preferences when they choose these k

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Alexander Wei?e,Gerhard Wellein,Holger Fehskes complex, generic CNN architectures that can be used for multiple tasks (i.e., as a pretrained model). This is achieved through cartesian genetic programming (CGP) for neural architecture search (NAS). Our approach integrates self-supervised learning with a progressive architecture search process.

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M. Dreher,D. Fischer,K. Franzrahe,P. Henseler,J. Hoffmann,W. Strepp,P. Nielaba and versatility. As the use of these models continues to grow, the importance of effective prompt engineering has come to the fore. Prompt optimization emerges as a crucial challenge, as it has a direct impact on model performance and the extraction of relevant information. Recently, evolutionary a

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Catia Lavalle,M. Rigol,M. Feldbacher,Fakher F. Assaad,Alejandro Muramatsusively that mainly solve steady-state situations, ignoring uncertainties of state variables as well as their near-future. Thus, in a dynamic and uncertain power system, where the demand as well as the supply-side show volatile behavior, optimization methods are needed that provide solutions very qui

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omplex or factory. However, one needs to consider a multitude of upgrade options and several performance criteria. We therefore formulated this task as a many-objective optimization problem with 10 design parameters and 5 objectives (investment cost, yearly energy costs, . emissions, system resilien

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Parameter Dependences of Convection Driven Spherical Dynamos Stuttgart Supercomputing Center. Studies of the dependence on the Prandtl number . indicate that dynamo action disappears with increasing . unless the magnetic Prandtl number . . is also increased. Relaxation oscillations of convection coupled to magnetic torsional oscillations are found at low Pra