One of the main goals of signal analysis has been the development of signal representations in terms of elementary waveforms or atoms. Dictionaries are collections of atoms with common parameterized features. We present a pursuit methodology to optimize redundant atomic representations from several dictionaries. The architecture exploits notions of modularity and coadaptation between atoms, in order to evolve an optimized signal representation. Modularity is modeled by dictionaries. Coadaptation is promoted by introducing self-adaptive, gene expression weights associated with the genetic representation of a signal in a proper dictionary space. The proposed model is tested on atomic pattern recognition problems.