Ten lectures on wavelets
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Evolutionary-based methods for adaptive signal representation
Signal Processing
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Approximations with evolutionary pursuit
Signal Processing
Wavelet denoising with evolutionary algorithms
Digital Signal Processing
Combining mutation operators in evolutionary programming
IEEE Transactions on Evolutionary Computation
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
On self-adaptive features in real-parameter evolutionary algorithms
IEEE Transactions on Evolutionary Computation
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Wavelet shrinkage estimation has become an attractive and efficient method for signal denoising and compression. Despite the ample variety of methods which have been used in the wavelet denoising context, it has proven elusive to construct threshold estimators with good adaptive properties. Recently, empirical Bayes selection criteria have been proposed to derive adaptive shrinkage estimators. We consider the application of empirical Bayes variable selection criteria to each level of the wavelet transform to obtain adaptive threshold estimates. A set of level-dependent hyperparameters has to be estimated to derive nonlinear data-dependent thresholding rules. We propose the use of an evolutionary algorithm to calibrate the multilevel parameters, in order to automate parameter selection and enhance adaptivity of the threshold estimators. Comparative simulations on a set of standard model functions show good performance. Applications to data drawn from various fields of application are used to explore the practical performance of the proposed approach.