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.

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In Proceedings of the Genetic and Evolutionary Computation Conference, 969-980, San Francisco, 2004. Kaufmann.

@InProceedings{grasemann:gecco04, title={Evolving Wavelets using a Coevolutionary Genetic Algorithm and Lifting}, author={Uli Grasemann and Risto Miikkulainen}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference}, address={San Francisco}, publisher={Kaufmann}, pages={969-980}, url="http://nn.cs.utexas.edu/?grasemann:gecco04", year={2004} }