Reducing bias and inefficiency in the selection algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Dynamic Parameter Encoding for Genetic Algorithms
Machine Learning
Wavelets: a tutorial in theory and applications
Wavelets: a tutorial in theory and applications
An introduction to wavelets
Acoustic signal compression with wavelet packets
Wavelets: a tutorial in theory and applications
Polynomial splines and wavelets: a signal processing perspective
Wavelets: a tutorial in theory and applications
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Adapting Operator Probabilities in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Matching Pursuits with Time-Frequency Dictionaries
Matching Pursuits with Time-Frequency Dictionaries
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
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In this article, the usability of genetic algorithms for signal approximation is discussed. Due to recent developments in the field of signal approximation by wavelets, this work concentrates on signal approximation by wavelet-like functions. Signals are approximated by a finite linear combination of elementary functions and a genetic algorithm is employed to find the coefficients to such an approximation. The algorithm maintains a population of different approximations, encoded in the form of 'chromosomes'. From this population 'parents' are selected according to their 'fitness', and the 'children' that constitute the next generation are produced from these parents using mutation and crossover operators.Fitness functions employed to evaluate different approximations are the L1, L2, L4, and L∞ norms. Experiments are carried out on several test signals, using Gabor and spline wavelets, both to evaluate the quality of different fitness functions, encoding schemes, and operators, and to assess the usefulness of genetic algorithms in the realm of signal approximation.Although other existing methods are faster while providing comparable approximation quality, the algorithm offers a great deal of flexibility in terms of different elementary functions, fitness criteria, etc.