Oliver König

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Instruction selection for embedded processors is a challenging problem. Embedded system architectures feature highly irregular instruction sets and complex data paths. Traditional code generation techniques have difficulties to fully utilize the features of such architectures and typically result in inefficient code. In this paper we describe an instruction(More)
When it comes to solving nonconvex, discontin-uous, or discrete problems in Structural Optimization (e.g. maximizing first eigenfrequency of a structure), the use of computationally expensive Genetic Algorithms (GA's) gets interesting. GA's are stochastic optimization algorithms based on natural selection and genetics. In contrast to traditional(More)
This paper evaluates the feasibility of applying the Lin-Kernighan algorithm to the problem of optimizing cutting and welding paths. A methodology was developed to solve this problem that is based on modifying the coordinate data presented to the Lin-Kernighan algorithm and to interpret the results of this optimization to ensure the problem constraints are(More)
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