Efficient implementation of matching pursuit using a genetic algorithm in the continuous space

In this work we introduce an alternative implementation of matching pursuit (MP) using a genetic algorithm in the continuous space (GACS). MP is an attractive analysis approach in which the signal is sequentially decomposed into a linear expansion of atoms (functions) from a dictionary of waveforms so as to obtain a sparse representation. The main problem with MP is its computation load, due to the necessarily large size of the dictionary. We propose instead to determine the optimal atom at each stage of the decomposition using a GACS, i.e. a genetic algorithm that requires no quantization of the solution parameters. Preliminary simulation results illustrate the potential benefits of this scheme.