Ten lectures on wavelets
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Adapted wavelet analysis from theory to software
Adapted wavelet analysis from theory to software
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Evolutionary-based methods for adaptive signal representation
Signal Processing
A Course in Digital Signal Processing
A Course in Digital Signal Processing
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A Pursuit Architecture for Signal Analysis
Proceedings of the EvoWorkshops on Applications of Evolutionary Computing
Toward a theory of evolution strategies: Self-adaptation
Evolutionary Computation
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
A four-parameter atomic decomposition of chirplets
IEEE Transactions on Signal Processing
Combining mutation operators in evolutionary programming
IEEE Transactions on Evolutionary Computation
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
Quantized overcomplete expansions in IRN: analysis, synthesis, and algorithms
IEEE Transactions on Information Theory
Bayesian wavelet denoising and evolutionary calibration
Digital Signal Processing
Kernel matching pursuit based on immune clonal algorithm for image recognition
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
Genetic wavelet packets for speech recognition
Expert Systems with Applications: An International Journal
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In recent years there has been growing interest in nonlinear signal approximations and atomic decomposition of functions. We propose a new approach to optimize overcomplete decompositions from several dictionaries. The methodology, referred to as evolutionary pursuit, relies on evolutionary computation techniques to optimize well-adapted approximations. Searching for the best approximation for a given signal is viewed as a stochastic optimization process. Stochastic perturbation parameters are introduced to improve both stability and robustness in the process of combining components from multiple dictionaries. The utility of these parameters in the analysis is justified in terms of the theory of frames, and tested in applications. The proposed method is applied to a collection of representative experimental tests, and the results compared with equivalent results from alternative approaches.