In this paper, we propose an approach for efficient approximative RkNN search in arbitrary metric spaces where the value of k is specified at query time. Our method uses an approx...
— We present the memetic climber, a simple search algorithm that learns topology and weights of neural networks on different time scales. When applied to the problem of learning ...
This paper presents an Integrated Framework of Design Optimization and Space Minimization (IDOM) for generating the minimum number of functional units with schedule length and mem...
The paper addresses the region search problem in three-dimensional (3D) space. The data used is a dynamically growing point cloud as it is typically gathered with a 3D-sensing devi...
The task of automatic design space exploration of heterogeneous multi-processor systems is often tackled with Evolutionary Algorithms. In this paper, we propose a novel approach i...
Thomas Schlichter, Christian Haubelt, Frank Hannig...