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Evolving Wavelets Using a Coevolutionary Genetic Algorithm and Lifting
Uli Grasemann and Risto Miikkulainen
Department of Computer Sciences, University of Texas at Austin, Austin, TX 78712, USAuli@cs.utexas.edu
risto@cs.utexas.edu
Abstract. Finding a good wavelet for a particular application and type of input data is a difficult problem. Traditional methods of wavelet design focus on abstract properties of the wavelet that can be optimized analytically but whose influence on its real-world performance are not entirely understood. In this paper, a coevolutionary genetic algorithm is developed that searches the space of biorthogonal wavelets. The lifting technique, which defines a wavelet as a sequence of digital filters, provides a compact representation and an efficient way of handling necessary constraints. The algorithm is applied to a signal compression task with good results.
LNCS 3103, p. 969 ff.
lncs@springer.de
© Springer-Verlag Berlin Heidelberg 2004