Evolving Wavelets using a Coevolutionary Genetic Algorithm and Lifting (2004)
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.
In Proceedings of the Genetic and Evolutionary Computation Conference, 969-980, San Francisco, 2004. Kaufmann.

Uli Grasemann Postdoctoral Alumni uli [at] cs utexas edu
Risto Miikkulainen Faculty risto [at] cs utexas edu