Design of Fast Multidimensional Filters Using Genetic Algorithms

A method for designing fast multidimensional filters using genetic algorithms is described. The filter is decomposed into component filters where coefficients can be sparsely scattered using filter networks. Placement of coefficients in the filters is done by genetic algorithms and the resulting filters are optimized using an alternating least squares approach. The method is tested on a 2-D quadrature filter and the method yields a higher quality filter in terms of weighted distortion compared to other efficient implementations that require the same ammount of computations to apply. The resulting filter also yields lower weighted distortion than the full implementation.

• Type: Conference
• Year: 2005
• Where: EVOW
• Authors: Max Langer, Björn Svensson, Anders Brun, Mats T. Andersson, Hans Knutsson
• Keywords: Artificial Intelligence, EVOW 2005, Fast Multidimensional filters, Genetic Algorithms, Weighted Distortion