A Pursuit Architecture for Signal Analysis

Authors:
Adelino R. Ferreira da Silva
Affiliations:
-
Venue:
Proceedings of the EvoWorkshops on Applications of Evolutionary Computing
Year:
2001

Citing 4
Cited 1

Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms

Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Evolutionary Wavelet Bases in Signal Spaces

Real-World Applications of Evolutionary Computing, EvoWorkshops 2000: EvoIASP, EvoSCONDI, EvoTel, EvoSTIM, EvoROB, and EvoFlight
Toward a theory of evolution strategies: Self-adaptation

Evolutionary Computation
Combining mutation operators in evolutionary programming

IEEE Transactions on Evolutionary Computation

Approximations with evolutionary pursuit

Signal Processing

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Abstract

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.